Download Data Mining Techniques for Medical Applications: A Survey

Mathematical Methods in Science and Mechanics
Data Mining Techniques for Medical Applications: A Survey
Ibrahim M. El-Hasnony, Hazem M. El Bakry, Ahmed A. Saleh
Faculty of Computer Science & Information Systems,
Mansoura University, Mansoura, EGYPT
Abstract- Data mining has been used to uncover hidden patterns and relations to summarize the data in ways to be useful and
understandable in all types of businesses to make prediction for future perspective. Medical data is consider most famous
application to mine that data, so in this paper we introduce a survey on how medical data problems such as dealing with noisy,
incomplete , heterogeneous and intensive data has been faced ,the advantages and disadvantages of each one , finally suggest a
framework for enhancing and overcoming this problems. The theory of fuzzy sets has been recognized as a suitable tool to model
several kinds of patterns that can hold in data. In this paper, we are concerned with the development of a general model to
discover association rules among items in a (crisp) set of fuzzy transactions. This general model can be particularized in several
ways; each particular instance corresponds to a certain kind of pattern and/or repository of data. We describe some applications of
this scheme, paying special attention to the discovery of fuzzy association rules .to extract association rules from quantitative
data, the dataset at hand must be partitioned into intervals, and then converted into Boolean type .fuzzy association rules are
developed as a sharp knife by handling quantitative data using fuzzy set. Along with the proposed system we will use neural
network approaches for clustering, classification, statistical analysis and data modeling.
Keywords- Data Mining, association rules, fuzzy association rule, neural network..
collection of data objects, similar data are taking in the same
cluster, dissimilar data are taking in different clusters [2].
1. Introduction
Data mining is a step of analyzing in “Knowledge Discovery
and Data Mining" process, or KDD, data mining involve
methods for computational discovery of patterns in large data
sets such as artificial intelligence, machine learning, statistics
and database systems. It tasks has been categorized to
descriptive and predictive methods. Classification, clustering
and rule association mining are most common techniques for
descriptive and predictive analysis [1-2].
Association:
Association analysis is the discovery of association rules. It
depends on the frequency of transactional data occur together
in database, also depends on a threshold called support, and
identifies the frequent item sets.
Association data mining aimed to find association between
attributes, generate rules from data sets [2].
The association rule mining role is to reach all rules having
support≤ minsup (minimum support) threshold and confidence
≤minconf (minimum confidence) threshold [3].
2. Data mining consists of major elements
Classification:
Classification is the representation of data in given classes.
Which called supervised classification, it uses given class
labels to order the objects in the data collection [2].
Classification consider as an important task of data mining.
Using this approach data must be defined as class label (target
attribute).
In binary classification, the target attribute has only two
possible values: for example, high or low. Multiclass targets
have more than two values: for example, low, medium, high,
or unknown.
Classification can applied into Business modeling, marketing,
credit analysis, biomedical and drug response modeling.
Many papers have been devoted to develop algorithms to mine
ordinary association rules. The early efficient algorithms like
Apriori and AprioriTid [27], SETM [28], OCD [29], and DHP
[30] were continued with more recent developments like DIC
[31], CARMA [32], TBAR [33], and FP-Growth [34].
3. Fuzzy association rules
Association rules can be merged with many techniques i.e.
fuzzy rules.
Clustering:
Crisp rule
Crisp set theory uses one of only two values: true or false.
Crisp set cannot represent vague concepts [6]. Elements are
assigned to the sets by giving them the values 0 or 1. Every
element with 1 value is a member of the set, elements with 0
value is non-member of the set. The number of elements that
belong to a set is called its cardinality [7].
Clustering is the representation of data in classes. However,
unlike classification, in clustering, class labels are unknown
and it is up to the clustering algorithm to discover acceptable
classes. This called unsupervised classification. Clustering is a
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Mathematical Methods in Science and Mechanics
different results for the same input data based on
system state (time) [11].
Fuzzy rule
Fuzzy sets represented as an extension of the classical crisp
sets [7]. Fuzzy set theory is that an element belongs to a fuzzy
set with a certain degree of membership. Thus, a proposition
is not either true or false, but may be partly true or partly false
to any degree. This degree is usually taken as a real number in
the interval [0, 1] [5-6].
5. Data mining applications
Medical data mining:
Over the past decade, nudged by new federal regulations,
hospitals and medical offices around the country have been
converting scribbled doctors’ notes to electronic records.
Although the chief goal has been to improve efficiency and cut
costs [13].
Fuzzy rules can be combined with Association rules to
generate fuzzy association rules.
There exists a new approach that use classical association rule
mining by using fuzzy sets. Fuzzy association mining solves
many problems find in huge quantities of data that exist often
in database efficiently [7].
When dividing an attribute in the data into sets covering
certain ranges of values, we are confronted with the sharp
boundary problem. Elements near the boundaries of a crisp set
will either be ignored.
Spatial data mining:
Spatial data mining is the application of data mining methods
to spatial data. The end objective of spatial data mining is to
find patterns in data with respect to geography. So far, data
mining and Geographic Information Systems (GIS) have
existed as two separate technologies, each with its own
methods, traditions, and approaches to visualization and data
analysis. Particularly, most contemporary GIS have only very
basic spatial analysis functionality. The immense explosion in
geographically referenced data occasioned by developments in
IT, digital mapping, remote sensing, and the global diffusion
of GIS emphasizes the importance of developing data driven
inductive approaches to geographical analysis and modeling
[35-119].
Sensor data mining:
Wireless sensor networks can be used for facilitating the
collection of data for spatial data mining for a variety of
applications such as air pollution monitoring. A characteristic
of such networks is that nearby sensor nodes monitoring an
environmental feature typically registers similar values. This
kind of data redundancy due to the spatial correlation between
sensor observations inspires the techniques for in-network data
aggregation and mining. By measuring the spatial correlation
between data sampled by different sensors, a wide class of
specialized algorithms can be developed to develop more
efficient spatial data mining algorithms [21].
Visual data mining:
In the process of turning from analogical into digital, large data
sets have been generated, collected, and stored discovering
statistical patterns, trends and information which is hidden in
data, in order to build predictive patterns. Studies suggest
visual data mining is faster and much more intuitive than is
traditional data mining [22].
Music data mining:
Data mining techniques, and in particular co-occurrence
analysis, has been used to discover relevant similarities among
music corpora (radio lists, CD databases) for purposes
including classifying music into genres in a more objective
manner [23].
Pattern mining:
"Pattern mining" is a data mining method that involves finding
existing patterns in data. In this context patterns often means
association rules. The original motivation for searching
association rules came from the desire to analyze supermarket
transaction data, that is, to examine customer behavior in terms
4. Neural Networks in Data Mining
Neural networks have been successfully applied in supervised
and unsupervised learning applications .There are two classes
of approaches for data mining with neural networks .The first
approach called rule extraction involves model extraction from
trained neural networks ,The second approach is to directly
learn simple easy-to-understand networks [8].
Neural Network Applications can be grouped in following
categories
•
Clustering:
A clustering algorithm explores the similarity
between patterns and places similar patterns in a
cluster. Best known applications include data
compression and data mining [9].
•
Classification/Pattern recognition:
The task of pattern recognition is to assign an input
pattern (like handwritten symbol) to one of many
classes. This category includes algorithmic
implementations such as associative memory [10].
•
Function approximation:
The tasks of function approximation is to find an
estimate of the unknown function f () subject to noise.
Various engineering and scientific disciplines require
function approximation [11].
•
Prediction/Dynamical Systems:
The task is to forecast some future values of a time
sequenced data. Prediction has a significant impact on
decision support systems. Prediction differs from
Function approximation by considering time factor.
Here the system is dynamic and may produce
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Mathematical Methods in Science and Mechanics
•
of the purchased products. For example, an association rules
"beer Potato chips (80)" states that four out of five customers
that bought beer also bought potato chips.
In the context of pattern mining as a tool to identify terrorist
activity, the National Research Council provides the following
definition: "Pattern-based data mining looks for patterns
(including anomalous data patterns) that might be associated
with terrorist activity these patterns might be regarded as small
signals in a large ocean of noise"[24][25]. Pattern Mining
includes new areas such a Music Information Retrieval (MIR)
where patterns seen both in the temporal and non-temporal
domains are imported to classical knowledge discovery search
methods.
Subject-based data mining:
"Subject based data mining" is a data mining method involving
the search for associations between individuals in data. In the
context of combating terrorism, the National Research Council
provides the following definition: "Subject-based data mining
uses an initiating individual or other datum that is considered,
based on other information, to be of high interest, and the goal
is to determine what other persons or financial transactions or
movements, etc., are related to that initiating datum" [26].
•
•
•
•
Many of the environments still lacks standards that impede the
use and analysis of data on a wide range of global data,
limiting this application to data sets collected for specific
diagnostic, screening, prognostic, monitoring, therapy support
or other patient management purposes [14].
6.2 Related work
Here, we make a comparison among different studies that
relate to our study. Many researchers aimed to reach more
accurate and complete system in knowledge discovery .These
include practical issues such as handling noisy and incomplete
data (e.g. protein interactions have high false positive and false
negative rates). Fuzzy association rule can be applied to solve
this problem. Association rule mining is one of the
fundamental tasks of data mining. The conventional
association rule mining algorithms, using crisp set, are meant
for handling Boolean data. Therefore, to extract association
rules from quantitative data, the dataset at hand must be
partitioned into intervals, and then converted into Boolean
type. In the sequel, it may suffer with the problem of sharp
boundary. Hence, fuzzy association rules are developed as a
sharp knife to solve problem by handling quantitative data
using fuzzy set.
Another problem in knowledge discovery is, Modern medicine
generates almost daily, huge amounts of heterogeneous data.
For example, medical data may contain SPECT images,
signals like ECG, clinical information like temperature,
cholesterol levels, etc., as well as the physician's interpretation.
Those who deal with such data understand that there is a
widening gap between data collection and data comprehension.
Computerized techniques are needed to help humans address
this problem. This volume is devoted to the relatively young
and growing field of medical data mining and knowledge
discovery. As more and more medical procedures employ
imaging as a preferred diagnostic tool, there is a need to
develop methods for efficient mining in databases, chi-square
or correlation that is used in clustering can be applied to solved
this problem, Correlation clustering also relates to a different
task, where correlations among attributes of feature vectors in
a high-dimensional space are assumed to exist guiding the
clustering process. These correlations may be different in
different clusters, thus a global decorrelation cannot reduce
this to traditional (uncorrelated) clustering. Correlations among
subsets of attributes result in different spatial shapes of
clusters. Hence, the similarity between cluster objects is
defined by taking into account the local correlation
6. Data mining in medical data
Modern medicine generates large amount of information stored
in the medical database. It is necessary to extract useful
knowledge and providing scientific decision-making for the
diagnosis and treatment of disease from the database
increasingly becomes necessary. Data mining in medicine can
deal with this problem. It can also improve the management
quality of hospital information and promote the development
of telemedicine and community medicine. Because the medical
information is characteristic of redundancy, multi-attribution,
incompletion and closely related with time, medical data
mining differs from other one. In this paper we have discussed
the key techniques of medical data mining involving
pretreatment of medical data, fusion of different pattern and
resource, fast and robust mining algorithms and reliability of
mining results. The methods and applications of medical data
mining based on computation intelligence such as artificial
neural network, fuzzy system, evolutionary algorithms, rough
set, and association rules have been introduced [12][13].
6.1 Problems in medical data
Extensive amounts of knowledge and data stored in medical
database need us to develop specialized tools for accessing,
data analysis, knowledge discovery and effective use of stored
knowledge and data, Because of the increase of data volume
results in difficulties in extracting useful information for
decision support. The traditional manual data analysis has
become insufficient.
Important issues that result from the rapidly emerging
inclusive of data and information are:
ISBN: 978-960-474-396-4
The provision of standard in terminology,
vocabularies and formats to support multi-liguity and
sharing of data.
Standards for the abstraction and visualization of
data.
Integration of heterogeneous types of data including
image and signals ...etc
Standards for interfaces between different resources
of data.
Reusability of data, knowledge and tools.
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Mathematical Methods in Science and Mechanics
algorithmic systems which use historical data to identify
trends, clusters, and patterns. Unsupervised neural networks
(clustering) (i.e. SOM, Hebbian law). Supervised learning are
limited by their training, i.e. they can reliably recognize only
the kind of information on which they were trained (i.e.
Perceprton , Multilayer NN).
Fuzzy association system is applied to solve several problems
that faces data mining ,Fuzzy rules can be combined with
association rules algorithm (Apriori or FP-Growth ),system
receive input data then attribute will be selected for operations
,then selected data will be transform to linguistic variable by
applying fuzzy membership function ,fuzzy variables
transformed to crisp values that can generate rules easily, The
user defined support will be inserted and frequent pattern is
generated, then confidence inserted and system association
rules generated ,after those steps the output of fuzzy linguistic
variable that is crisp values combined with association rules
that generate frequent fuzzy association rules, with the
frequent fuzzy association rule the problem with noisy or
incomplete data will be solved.
Also ,transformed data will be the Input for Neural Network
that may be supervised NN (i.e. perceptron ,Multilayer NN)
that used in data classification , unsupervised NN(i.e. SOM
,Hebbian law) used for clustering of data ,using data clustering
,the problem of heterogeneous data and problem of intensive
data will be solved.
7. The Proposed Framework
Many researchers aimed to reach more accurate and complete
system in knowledge discovery. These include practical issues
such as handling noisy and incomplete data (e.g. protein
interactions have high false positive and false negative rates).
Fuzzy association rule can be applied to solve this problem.
Association rule mining is one of the fundamental tasks of data
mining. The conventional association rule mining algorithms,
using crisp set, are meant for handling Boolean data.
Therefore, to extract association rules from quantitative data,
the dataset at hand must be partitioned into intervals, and then
converted into Boolean type. In the sequel, it may suffer with
the problem of sharp boundary. Hence, fuzzy association rules
are developed as a sharp knife to solve problem by handling
quantitative data using fuzzy set.
Another problem in knowledge discovery is, Modern medicine
generates almost daily, huge amounts of heterogeneous data.
For example, medical data may contain SPECT images,
signals like ECG, clinical information like temperature,
cholesterol levels, etc., as well as the physician's interpretation.
Those who deal with such data understand that there is a
widening gap between data collection and data comprehension.
Computerized techniques are needed to help humans address
this problem. This volume is devoted to the relatively young
and growing field of medical data mining and knowledge
discovery. As more and more medical procedures employ
imaging as a preferred diagnostic tool, there is a need to
develop methods for efficient mining in databases, chi-square
or correlation that is used in clustering can be applied to solved
this problem, Correlation clustering also relates to a different
task, where correlations among attributes of feature vectors in
a high-dimensional space are assumed to exist guiding the
clustering process. These correlations may be different in
different clusters, thus a global decorrelation cannot reduce
this to traditional (uncorrelated) clustering. Correlations among
subsets of attributes result in different spatial shapes of
clusters. Hence, the similarity between cluster objects is
defined by taking into account the local correlation patterns.
With this notion, the term has been introduced in
simultaneously with the notion discussed above. Different
methods for correlation clustering of this type are discussed in,
the relationship to different types of clustering is discussed in,
see also clustering high-dimensional data.
Other problem is processing compute intensive tasks (e.g.
large scale graph mining) i.e. Big data ,Big data is the term for
a collection of data sets so large and complex that it becomes
difficult to process using on hand database management tools
or traditional data processing applications. The challenges
include capture, storage, search, sharing, transfer, analysis and
visualization. Neural network used in classification or
clustering, a neural network is an interconnected assembly of
simple processing elements, units or nodes whose functionality
is loosely based on the animal neuron. The processing ability
of the network is stored in the inter-unit connection strengths,
or weights, obtained by a process of adaption to, or learning
from a set of training patterns. Neural Networks are
ISBN: 978-960-474-396-4
8. Conclusion
Modern medicine generate large amount of information stored
in medical database, these extensive amounts of knowledge
and data in medicine need us to develop specialized tools for
accessing data analysis , knowledge discovery and effective
use of stored knowledge and data. In this paper, include three
main practical issues: Handling noisy and incomplete data,
Generating almost daily huge amounts of heterogeneous data,
processing compute intensive tasks. We suggest here in this
study data mining techniques as Fuzzy association rules and
neural network techniques. Knowledge management is
providing the facility to find out these rules any time when
need.
REFERENCES
[1] Abdullah
SaadAlmalaiseAlghamdi
(2011)"Efficient
Implementation of FP Growth Algorithm-Data Mining on
Medical Data".
[2] Pang-Ning Tan, Michael Steinbach ,Vipin Kumar(2005),
"Introduction to Data Mining", Addison Wesley.
[3] Jiawei Han ,(2006)Data Mining - Concepts and Techniques - 2nd
Edition –Impressao.
[4] Ion
IANCU1,
Mihai
GABROVEANU1,
Adrian
GIURC(2000),"Some Quality Measures for Fuzzy Association
Rules"
[5] Michael Negnevitsky,(2005)A Guide to Intelligent Systems
,second edition.
[6] Bakk. Lukas Helm(2007),"Fuzzy Association Rules", An
Implementation in R.
208
Mathematical Methods in Science and Mechanics
[33] J. Han, J. Pei, and Y. Yin(2000) ,“Mining frequent patterns
without candi- date generation".
[34] Diansheng Guoa ,Jeremy Mennis(2009)" Spatial data mining and
geographic knowledge discovery".
[35] Hazem M. El-Bakry, Nikos E. Mastorakis, Michael E. Fafalios,
“Fast Information Retrieval from Big Data by using Cross
Correlation in the Frequency Domain,” Proc. of IEEE IJCNN
2013, Dallas Tx, USA, August 4-9, 2013, pp. 366-272.
[36] Ahmed M. El-Zehery, Hazem M. El-Bakry, Mohamed S. ElKsasy, “Applying Data Mining Techniques for Customer
Relationship Management: A Survey,” International Journal of
Computer Science and Information Security, vol. 11, no.
11, November, 2013, pp. 76-82.
[37] Hazem M. El-Bakry, and Mohamed Hamada, “Fast Diagnosing
of Pediatric Respiratory Diseases by using High Speed Neural
Networks,” Proc. of IEEE IJCNN 2013, Dallas Tx, USA, August
4-9, 2013, pp. 226-232.
[38] Hazem M. El-Bakry, and Nikos Mastorakis, “A New Fast Neural
Model,” Proceedings of the 11th WSEAS international conference
on Applied Computer and Applied Computational Science
(ACACOS'12), Rovaniemi, Finland, April 18-20, 2012, pp 224231
[39] Hazem M. El-Bakry, "An Efficient Algorithm for Pattern
Detection using Combined Classifiers and Data Fusion,"
Information Fusion Journal, vol. 11, 2010, pp. 133-148.
[40] Hazem M. El-Bakry, "A Novel High Speed Neural Model for
Fast Pattern Recognition," Soft Computing Journal, vol. 14, no.
6, 2010, pp. 647-666.
[41] Hazem M. El-Bakry, "Fast Virus Detection by using High Speed
Time Delay Neural Networks," Journal of Computer Virology,
vol. 6, no. 2, 2010, pp. 115-122.
[42] Hazem M. El-Bakry, and Nikos Mastorakis, “An Intelligent
Approach for Fast Detection of Biological Viruses in DNA
Sequence,” Proc. of 10th WSEAS International Conference on
APPLICATIONS of COMPUTER ENGINEERING (ACE '11),
Spain, March 24-26, 2011, pp. 237-244.
[43] Hazem M. El-bakry, and Nikos Mastorakis, “A New Approach
for Prediction by using Integrated Neural Networks,” Proc. of
Int. Conf., Mexico, Jan. 29-31, 2011, pp. 17-28.
[44] Hazem M. El-Bakry, “Fast Karnough Map for Simplification of
Complex Boolean Functions,” Proc. of 10th WSEAS
International Conference on Applied Computer Science
(ACS'10), Japan, October 4-6, 2010, pp. 478-483.
[45] Hazem M. El-bakry, and Nikos Mastorakis, “Prediction of
Market Price by using Fast Time Delay Neural Networks,” Proc.
of 10th WSEAS Int. Conf. on Neural Networks (NN'10),
Romania, June 13-15, 2010, pp. 230-237.
[46] Hazem M. El-bakry, and Nikos Mastorakis, “Fast Forecasting of
Stock Market Prices by using New High Speed Time Delay
Neural Networks,” Waset International Journal of Computer and
Information Engineering, vol. 4, no.2., 2010, pp. 138-144.
[47] Hazem M. El-bakry, and Nikos Mastorakis, “Fast Packet
Detection by using High Speed Time Delay Neural Networks,"
Proc. of the 10th WSEAS Int. Conference on Multimedia Systems
& Signal Processing, Hangzhou University, China, April 11-13,
2010, pp. 222-227.
[48] Hazem M. El-Bakry, Alaa M. Riad, Ahmed Atwan, Sameh Abd
El-Ghany, and Nikos Mastorakis “A New Automated
Information Retrieval System by using Intelligent Mobile Agent,
" Proc. of Recent Advances in Artificial Intelligence, Koweledge
Engineering and Databases, Cambridge, UK, February 20-22,
2010, pp. 339-351.
[7] Mark W Craven, Jude W Shavlik(1997) "Using Neural Networks
for Data Mining".
[8] P.Dostál,P.Pokorný(2009)"CLUSTER
ANALYSIS
AND
NEURAL NETWORK ".
[9] David Reby , Sovan Lek , Ioannis Dimopoulos , Jean Joachim ,
Jacques Lauga ,Ste´phane Aulagnier (1996)"Artificial neural
networks as a classification method in thebehavioural sciences".
[10] ZARITA ZAINUDDIN , ONG PAULINE (2007)" Function
Approximation Using Artificial Neural Networks ".
[11] Yilmaz
GOKSEN,
Mete
EMINAGAOGLU,
Onur
DOGAN(2011)"DATA MINING IN MEDICAL RECORDS
FOR THE ENHANCEMENT OF STRATEGIC DECISIONS".
[12] Yi Mao, Yixin Chen, Gregory Hackmann, Minmin Chen,
Chenyang Lu, Marin Kollef, Thomas C.Bailey(2011)"Medical
Data Mining for Early Deterioration Warning in General Hospital
Wards".
[13] Nada Lavrac, BlažZupan(2005)"Data Mining in Medicine"in
Data Mining and Knowledge Discovery Handbook.
[14] Sandra
Liewis,
Liangxiu
Han
and
John
A.
Keane(2013)"Understanding Low Back Pain using Fuzzy
Association Rule Mining".
[15] D. S. Rajput, R. S. Thakur and G. S. Thakur(2014)"Karnaugh
Map Approach for Mining Frequent Termset from Uncertain
Textual Data".
[16] Ranjita Singh, Sreeja Nair (2014)"International Journal of
Advanced Research in
Computer Science and Software
Engineering ".
[17] Ion IANCU and Mihai GABROVEANU (2010)"Fuzzy Logic
Controller Based on Association Rules".
[18] OlaiyaFolorunsho(2013) "Comparative Study of Different Data
Mining Techniques Performance in knowledge Discovery from
Medical Database ".
[19] Sahilpreet Singh , MeenakshiBansal (2013)"Improvement of
Intrusion Detection System in Data Mining using Neural
Network".
[20] Ma, Y.; Guo, Y.; Tian, X.; Ghanem, M. (2011). "Distributed
Clustering-Based Aggregation Algorithm for Spatial Correlated
Sensor Networks". IEEE Sensors Journal 11 (3): 641.
[21] Keim, Daniel A.(2002)" Information Visualization and Visual
Data Mining".
[22] Pachet,
François;
Westermann,
Gert;
and
Laigre,
Damien(2001)"Musical Data Mining for Electronic Music
Distribution".
[23] Haag, Stephen; Cummings, Maeve; Phillips, Amy (2006)
"Management Information Systems for the information age".
[24] National Research Council (2008)"Protecting Individual Privacy
in the Struggle Against Terrorists.
[25] Ion IANCU and Mihai GABROVEANU (2010)"Fuzzy Logic
Controller Based on Association Rules".
[26] R. Agrawal and R. Srikant,(1994)“Fast algorithms for mining
association rules”.
[27] M. Houtsma ,A. Swami(1995)“Set-orientedmining for
associationrules in relational databases”.
[28] H. Mannila, H. Toivonen, and I. Verkamo(1994) ,“Efficient
algorithms for discovering association rules”.
[29] J.-S. Park, M.-S. Chen, and P. S. Yu (1995),“An effective hash
based algo- rithm for mining association rules” .
[30] S. Brin, R. Motwani, J. D. Ullman, and S. Tsur(1997)
,“Dynamic itemset counting and implication rules for market
basket data” .
[31] C. Hidber( 1999)”Online association rule mining”.
[32] F. Berzal, J. C. Cubero, N. Marín, and J. M. Serrano(2001)
“TBAR: An effi- cient method for association rule mining in
relational databases".
ISBN: 978-960-474-396-4
209
Mathematical Methods in Science and Mechanics
[49] Hazem M. El-Bakry, "New Fast Principal Component Analysis
For Real-Time Face Detection," Machine Graphics & Vision
Journal (MG&V), vol. 18, no.4, 2009, pp. 405-426.
[50] Hazem M. El-bakry, and Mohamed Hamada “Fast Time Delay
Neural Networks for Detecting DNA Coding Regions,” Springer,
Lecture Notes on Artificial Intelligence (LNAI 5711), 2009, pp.
334-342.
[51] Hazem M. El-Bakry, "A New Neural Design for Faster Pattern
Detection Using Cross Correlation and Matrix Decomposition,"
Neural World journal, Neural World Journal, 2009, vol. 19, no.
2, pp. 131-164.
[52] Hazem M. El-Bakry, and Ahmed Atwan, " Improving Quality of
Business Networks for Information Systems," The International
Journal of Computer, Information, and systems science, and
Engineering, issue 3, vol. 3, July 2009, pp. 138-150.
[53] Hazem M. El-Bakry, and Ahmed A. Mohammed, "Optimal
Document Archiving and Fast Information Retrieval," The
International Journal of Computer science, and Engineering,
issue 2, vol. 3, July 2009, pp. 108-121.
[54] Hazem M. El-Bakry, and Nikos Mastorakis, "Fast Word
Detection in a Speech Using New High Speed Time Delay
Neural Networks," WSEAS Transactions on Information Science
and Applications, issue 7, vol. 5, July 2009, pp. 261-270.
[55] Hazem M. El-Bakry, and Nikos Mastorakis, "Fast Information
Retrieval from Web Pages," WSEAS Transactions on
Information Science and Applications, issue 6, vol. 6, June 2009,
pp. 1018-1036.
[56] Hazem M. El-Bakry, and Nikos Mastorakis, "Fast Image
Matching on Web Pages," WSEAS Transactions on Signal
Processing, issue 4, vol. 5, June 2009, pp. 157-166.
[57] Hazem M. El-Bakry, and Nikos Mastorakis, "Fast Detection of
Specific Information in Voice Signal over Internet Protocol,"
WSEAS Transactions on Communications, issue 5, vol. 8, May
2009, pp. 483-494.
[58] Hazem M. El-bakry, and Nikos Mastorakis “Fast Time Delay
Neural Networks for Word Detection in a Video Conference,”
Proc. of European Computing and Computational Intelligence
International Conference, Tbilisi, Georgia, June 26-28, 2009, pp.
120-129.
[59] Alaa M. Riad, Hazem M. El-bakry, and Nikos Mastorakis, “Fast
Harmonic Current / Voltage Prediction by using High Speed
Time Delay Neural Networks, " Proc. of WSEAS International
Conference on Communication and Information, Athens, Greece,
December 29-31, 2009, pp. 245-272.
[60] Hazem M. El-Bakry, and Nikos Mastorakis “Fast Human
Motion Tracking by using High Speed Neural Networks " Proc.
of 9th WSEAS International Conference on SIGNAL, SPEECH
AND IMAGE PROCESSING (SSIP '09), Budapest, Hungry,
September 3-5, 2009, pp. 221-240.
[61] Hazem M. El-Bakry, and Nikos Mastorakis “A Fast
Computerized Method For Automatic Simplification of Boolean
Functions,” Proc. of 9th WSEAS International Conference on
SYSTEMS THEORY AND SCIENTIFIC COMPUTATION
(ISTASC '09), Moscow, Russia, August 26-28, 2009, pp. 99-107.
[62] Hazem M. El-Bakry, and Nikos Mastorakis “Fast Information
Processing over Business Networks,” Proc. of 9th WSEAS
International Conference on Applied Informatics and
Communications (AIC'09), Moscow, Russia, August 26-28,
2009, pp.305-324.
[63] Hazem M. El-Bakry, and Nikos Mastorakis “A Fast Searching
Protocol for Fully Replicated System,” Proc. of of 13th WSEAS
International Conference on Computers, Rodos, Greece, July 2225, 2009, pp. 588-600.
ISBN: 978-960-474-396-4
[64] Hazem M. El-Bakry, and Nikos Mastorakis “An Efficient
Electronic Archiving Approach for Office Automation,” Proc. of
European Computing and Computational Intelligence
International Conference, Tbilisi, Georgia, June 26-28, 2009, pp.
130-144.
[65] Hazem M. El-Bakry, and Nikos Mastorakis “Fast Time Delay
Neural Networks for Word Detection in a Video Conference,”
Proc. of European Computing and Computational Intelligence
International Conference, Tbilisi, Georgia, June 26-28, 2009, pp.
120-129.
[66] Hazem M. El-Bakry, “Fast Record Detection in Large Databases
Using New High Speed Time Delay Neural Networks,” Proc. of
IEEE IJCNN’09, Atlanta, USA, June 14-19, 2009, pp. 757-763.
[67] Hazem M. El-Bakry, and Mohamed Hamada “Fast Principal
Component Analysis for Face Detection Using Cross-Correlation
and Image Decomposition,” Proc. of IEEE IJCNN’09, Atlanta,
USA, June 14-19, 2009, pp. 751-756.
[68] Hazem M. El-Bakry, and Nikos Mastorakis “Fast Image
Matching on Web Pages,” Proc. of Recent Advances in Applied
Mathematics and Computational and Information Sciences,
Houston, USA, April 30-May 2, 2009, pp. 470-479.
[69] Hazem M. El-Bakry, and Nikos Mastorakis “Design of AntiGPS for Reasons of Security,” Proc. of Recent Advances in
Applied Mathematics and Computational and Information
Sciences, Houston, USA, April 30-May 2, 2009, pp. 480-500.
[70] Hazem M. El-Bakry, and Nikos Mastorakis, "A Modified
Hopfield Neural Network for Perfect Calculation of Magnetic
Resonance Spectroscopy," WSEAS Transactions on Information
Science and Applications, issue 12, vol. 5, December 2008, pp.
1654-1666.
[71] Hazem M. El-Bakry, and Nikos Mastorakis, "A New Fast
Forecasting Technique using High Speed Neural Networks,"
WSEAS Transactions on Signal Processing, issue 10, vol. 4,
October 2008, pp. 573-595.
[72] Hazem M. El-Bakry, and Nikos Mastorakis, "A New Technique
for Detecting Dental Diseases by using High Speed Artificial
Neural Network," WSEAS Transactions on Computers, Issue 12,
vol. 7, December 2008, pp. 1977-1987.
[73] Hazem M. El-Bakry, and Nikos Mastorakis, "A Real-Time
Intrusion Detection Algorithm for Network Security," WSEAS
Transactions on Communications, Issue 12, vol. 7, December
2008, pp. 1222-1234.
[74] Hazem M. El-Bakry, and Nikos Mastorakis, " An Effective
Method for Detecting Dental Diseases by using Fast Neural
Networks," WSEAS Transactions on Biology and Biomedicine,
issue 11, vol. 5, November 2008, pp. 293-301.
[75] Hazem M. El-Bakry, and Nikos Mastorakis, "A Novel Fast
Kolmogorov’s Spline Complex Network for Pattern Detection,"
WSEAS Transactions on Systems, Issue 11, vol. 7, November
2008, pp. 1310-1328.
[76] Hazem M. El-Bakry, "New Faster Normalized Neural Networks
for Sub-Matrix Detection using Cross Correlation in the
Frequency Domain and Matrix Decomposition, " Applied Soft
Computing journal, vol. 8, issue 2, March 2008, pp. 1131-1149.
[77] Hazem M. El-Bakry and Mohamed Hamada, "A New
Implementation for High Speed Neural Networks in Frequency
Space," Lecture Notes in Artificial Intelligence, Springer, KES
2008, Part I, LNAI 5177, pp. 33-40.
[78] Hazem M. El-Bakry, and Nikos Mastorakis “Fast Virus
Detection by using High Speed Time Delay Neural Networks,”
Proc. of 10th WSEAS Int. Conf. on NEURAL NETWORKS
(NN'09), Prague, Czech Repulic, March 22-25, 2008, pp. 169183.
210
Mathematical Methods in Science and Mechanics
[79] Hazem M. El-Bakry, and Nikos Mastorakis “New Efficient
Neural Networks for Fast Record Detection in Databases,” Proc.
of Recent Advances in Artificial Intelligence, Koweledge
Engineering and Databases, Cambridge, UK, February 21-23,
2009, pp. 95-102.
[80] Hazem M. El-Bakry, and Nikos Mastorakis “Fast Detection of
Specific Information in Voice Signal over Internet Protocol,”
Proc. of 7th WSEAS Int. Conf. on COMPUTATIONAL
INTELLIGENCE,
MAN-MACHINE
SYSTEMS
and
CYBERNETICS (CIMMACS '08), Cairo, EGYPT, Dec. 29-31,
2008, pp. 125-136.
[81] Hazem M. El-Bakry, and Nikos Mastorakis “Information
Retrieval Based on Image Detection on Web Pages,” Proc. of 7th
WSEAS Int. Conf. on COMPUTATIONAL INTELLIGENCE,
MAN-MACHINE SYSTEMS and CYBERNETICS (CIMMACS
'08), Cairo, EGYPT, Dec. 29-31, 2008, pp. 221-228.
[82] Hazem M. El-Bakry, and Nikos Mastorakis “Fast Information
Retrieval from Web Pages,” Proc. of 7th WSEAS Int. Conf. on
COMPUTATIONAL INTELLIGENCE, MAN-MACHINE
SYSTEMS and CYBERNETICS (CIMMACS '08), Cairo,
EGYPT, Dec. 29-31, 2008, pp. 229-247.
[83] Hazem M. El-Bakry and Mohamed Hamada, "New Fast
Decision Tree Classifier for Identifying Protein Coding
Regions," Proc. of ISICA 2008 Conf., China, Dec. 3-5, 2008, pp.
489-500.
[84] Hazem M. El-Bakry, and Nikos Mastorakis, " An Effective
Method for Detecting Dental Diseases by using Fast Neural
Networks, " 8th WSEAS International Conference on SIGNAL,
SPEECH AND IMAGE PROCESSING (SSIP '08), Santander,
Cantabria, Spain, September 23-25, 2008, pp. 144-152.
[85] Hazem M. El-Bakry, and Nikos Mastorakis, " A New Fast
Forecasting Technique using High Speed Neural Networks, " 8th
WSEAS International Conference on SIGNAL, SPEECH AND
IMAGE PROCESSING (SSIP '08), Santander, Cantabria, Spain,
September 23-25, 2008, pp. 116-138.
[86] Hazem M. El-Bakry, and Nikos Mastorakis, " Realization of EUniversity for Distance Learning, " 8th WSEAS International
Conference on DISTANCE LEARNING and WEB
ENGINEERING (DIWEB '08), Santander, Cantabria, Spain,
September 23-25, 2008, pp. 17-31.
[87] Hazem M. El-Bakry, and Nikos Mastorakis, " A Novel Fast
Kolmogorov's Spline Complex Network for Pattern Detection,"
8th WSEAS International Conference on SIMULATION,
MODELLING and OPTIMIZATION (SMO '08), Santander,
Cantabria, Spain, September 23-25, 2008, pp. 261-279.
[88] Hazem M. El-Bakry, and Nikos Mastorakis, " A New Technique
for Detecting Dental Diseases by using High Speed NeuroComputers," European Computing Conf. (ECC '08), Malta,
September 11-13, 2008, pp. 432-440.
[89] Hazem M. El-Bakry, and Nikos Mastorakis, " A Modified
Hopfield Neural Network for Perfect Calculation of Magnetic
Resonance Spectroscopy," 1st WSEAS International Conference
on Biomedical Electronics and Biomedical Informatics (BEBI
'08), Rhodes, Greece, August 20-22, 2008, pp. 242-254.
[90] Hazem M. El-Bakry, and Nikos Mastorakis, " A Real-Time
Intrusion Detection Algorithm for Network Security, " 8st
WSEAS International Conference on Applied Informatics and
Communications (AIC '08), Rhodes, Greece, August 20-22,
2008, pp. 533-545.
[91] Hazem M. El-Bakry, and Nikos Mastorakis "New Fast
Normalized Neural Networks for Pattern Detection," Image and
Vision Computing Journal, vol. 25, issue 11, 2007, pp. 17671784.
ISBN: 978-960-474-396-4
[92] Hazem M. El-Bakry, "New Fast Time Delay Neural Networks
Using Cross Correlation Performed in the Frequency Domain,"
Neurocomputing Journal, vol. 69, October 2006, pp. 2360-2363.
[93] Hazem M. El-Bakry and Nikos Mastorakis, "Fast Code
Detection Using High Speed Time Delay Neural Networks,"
Lecture Notes in Computer Science, Springer, vol. 4493, Part III,
May 2007, pp. 764-773.
[94] Hazem M. El-Bakry, "New High Speed Normalized Neural
Networks for Fast Pattern Discovery on Web Pages,"
International Journal of Computer Science and Network Security,
vol. 6, No. 2A, February 2006, pp. 142-152.
[95] Hazem M. El-Bakry, and Qiangfu Zhao, "Fast Normalized
Neural Processors For Pattern Detection Based on Cross
Correlation Implemented in the Frequency Domain," Journal of
Research and Practice in Information Technology, Vol. 38, No.2,
May 2006, pp. 151-170.
[96] Hazem M. El-Bakry, "New Fast Time Delay Neural Networks
Using Cross Correlation Performed in the Frequency Domain,"
Neurocomputing Journal, vol. 69, October 2006, pp. 2360-2363.
[97] Hazem M. El-Bakry, and Nikos Mastorakis, "A Novel Model of
Neural Networks for Fast Data Detection," WSEAS Transactions
on Computers, Issue 8, vol. 5, November 2006, pp. 1773-1780.
[98] Hazem M. El-Bakry, and Nikos Mastorakis, "A New Approach
for Fast Face Detection," WSEAS Transactions on Information
Science and Applications, issue 9, vol. 3, September 2006, pp.
1725-1730.
[99] Hazem M. El-Bakry, and Qiangfu Zhao, “Fast Neural
Implementation of PCA for Face Detection,” Proc. of IEEE
World Congress on Computational Intelligence, IJCNN’06,
Vancouver, BC, Canada, July 16-21, 2006, pp. 1785-1790.
[100] Hazem M. El-Bakry, “A Simple Design for High Speed
Normalized Neural Networks Implemented in the Frequency
Domain for Pattern Detection,” Proc. of IEEE World Congress
on Computational Intelligence, IJCNN’06, Vancouver, BC,
Canada, July 16-21, 2006, pp. 2296-2303.
[101] Hazem M. El-Bakry, “Fast Co-operative Modular Neural
Processors for Human Face Detection,” Proc. of IEEE World
Congress on Computational Intelligence, IJCNN’06, Vancouver,
BC, Canada, July 16-21, 2006, pp. 2304-2311.
[102] Hazem M. El-Bakry, “New Fast Time Delay Neural Networks
Using Cross Correlation Performed in the Frequency Domain,”
Proc. of IEEE World Congress on Computational Intelligence,
IJCNN’06, Vancouver, BC, Canada, July 16-21, 2006, pp. 49904997.
[103] Hazem M. El-Bakry, and Nikos Mastorakis, “A Novel Model
of Neural Networks for Fast Data Detection,” Proc. of the 7th
WSEAS International Conference on Neural Networks, Cavtat,
Croatia, June 12-14, 2006, pp. 144-151.
[104] Hazem M. El-Bakry, and Nikos Mastorakis, “A New Approach
for Fast Face Detection,” Proc. of the 7th WSEAS International
Conference on Neural Networks, Cavtat, Croatia, June 12-14,
2006, pp. 152-157.
[105] Hazem M. El-Bakry, "Pattern Detection Using Fast
Normalized Neural Networks," Lecture Notes in Computer
Science, Springer, vol. 3696, September 2005, pp. 447-454.
[106] Hazem M. El-Bakry, "Human Face Detection Using New High
Speed Modular Neural Networks," Lecture Notes in Computer
Science, Springer, vol. 3696, September 2005, pp. 543-550.
[107] Hazem M. El-Bakry, and Qiangfu Zhao, "Fast Pattern
Detection Using Normalized Neural Networks and Cross
Correlation in the Frequency Domain," EURASIP Journal on
Applied Signal Processing, Special Issue on Advances in
Intelligent Vision Systems: Methods and Applications—Part I,
vol. 2005, no. 13, 1 August 2005, pp. 2054-2060.
211
Mathematical Methods in Science and Mechanics
[108] Hazem M. El-Bakry, and Qiangfu Zhao, "Fast Time Delay
Neural Networks," International Journal of Neural Systems, vol.
15, no.6, December 2005, pp. 445-455.
[109] Hazem M. El-Bakry, and Qiangfu Zhao, "Speeding-up
Normalized Neural Networks For Face/Object Detection,"
Machine Graphics & Vision Journal (MG&V), vol. 14, No.1,
2005, pp. 29-59.
[110] Hazem M. El-Bakry, and Qiangfu Zhao, "A New Technique
for Fast Pattern Recognition Using Normalized Neural
Networks," WSEAS Transactions on Information Science and
Applications, issue 11, vol. 2, November 2005, pp. 1816-1835.
[111] Hazem M. El-Bakry, and Qiangfu Zhao, "Fast Complex
Valued Time Delay Neural Networks," International Journal of
Computational Intelligence, vol.2, no.1, pp. 16-26, 2005.
[112] Hazem M. El-Bakry, and Qiangfu Zhao, "Fast Pattern
Detection Using Neural Networks Realized in Frequency
Domain," Enformatika Transactions on Engineering, Computing,
and Technology, February 25-27, 2005, pp. 89-92.
[113] Hazem M. El-Bakry, "A New High Speed Neural Model For
Character Recognition Using Cross Correlation and Matrix
Decomposition," International Journal of Signal Processing,
vol.2, no.3, 2005, pp. 183-202.
[114] Hazem M. El-Bakry, and Qiangfu Zhao, "Face Detection
Using Fast Neural Processors and Image Decomposition,"
International Journal of Computational Intelligence, vol.1, no.4,
2004, pp. 313-316.
[115] Hazem M. El-Bakry, and H. Stoyan, "FNNs for Code
Detection in Sequential Data Using Neural Networks for
Communication Applications," Proc. of the First International
Conference on Cybernetics and Information Technologies,
Systems and Applications: CITSA 2004, pp. 21-25.
[116] Hazem M. El-Bakry, "Face detection using fast neural
networks and image decomposition," Neurocomputing Journal,
vol. 48, 2002, pp. 1039-1046.
[117] Hazem M. El-Bakry, "Human Iris Detection Using Fast
Cooperative Modular Neural Nets and Image Decomposition,"
Machine Graphics & Vision Journal (MG&V), vol. 11, no. 4,
2002, pp. 498-512.
[118] Hazem M. El-Bakry "Fast Iris Detection for Personal
Verification Using Modular Neural Networks," Lecture Notes in
Computer Science, Springer, vol. 2206, October 2001, pp. 269283.
[119] Hazem M. El-Bakry, "Automatic Human Face Recognition
Using Modular Neural Networks," Machine Graphics & Vision
Journal (MG&V), vol. 10, no. 1, 2001, pp. 47-73.
[120] Fayyad, U., Piatetsky-Shapiro, G.Smyth, P. (1996). From Data
Mining to Knowledge Discovery in Databases.AI Magazine,
17(3), 37-54.
Figure1: Data mining as step of Knowledge discovery process [120].
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Figure2: crisp and fuzzy rules [4]
Figure2: crisp and fuzzy rules [4]
ISBN: 978-960-474-396-4
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Height, cm