Download A Case of Data Mining - Global Vision Publishing House

Evolving Ideas
Computing, Communication and Networking
Publish by Global Vision Publishing House
Edited5by
5
Jeetendra Pande
Nihar Ranjan Pande
Deep Chandra Joshi
Interaction of Computer and Statistics:
A Case of Data Mining
Gaurav Shukla1, Pankaj Tiwari2 and Mohit Giri Goswami3
ABSTRACT
Data mining is emerging as one of the key features for various fields like as medical, agriculture, business
and also an important tool for homeland security efforts. Data mining represents a significant advance in the
type of analytical tools currently available. Data mining is becoming increasingly common in both the private
and public sectors. Industries such as banking, insurance, medicine, and retailing commonly use data
mining to reduce costs enhance research, and increase sales. In the public sector, data mining applications
initially were used as a means to detect fraud and waste, but have grown to also be used for purposes such
as measuring and improving program performance.
INTRODUCTION
Data mining, a branch of computer science is an analytic process of extracting the hidden predictive
information from large datasets by combining methods from statistics and artificial intelligence with database
management. It is a powerful new technology to help companies focus on the most important information
in their data warehouses. Data mining is seen as an increasingly important tool by modern business to
transform data into business intelligence giving an informational advantage. Data mining tools predict
future trends and behaviours, allowing businesses to make proactive, knowledge-driven decisions. The
automated, prospective analyses offered by data mining move beyond the analyses of past events provided
by retrospective tools typical of decision support systems. Data mining tools can answer business questions
1
2
3
LES Divison, IVRI, Izzatnagar, Bareilly (U.P.),Email: [email protected].
LES Divison, IVRI, Izzatnagar, Bareilly (U.P.) Email: [email protected].
AITS, Haldwani, Nainital (Uttrakhand) Email: [email protected].
Gaurav Shukla, Pankaj Tiwari and Mohit Giri Goswami
56
that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding
predictive information that experts may miss because it lies outside their expectations. It is currently used
in a wide range of profiling practices, such as marketing, surveillance, fraud detection, and scientific
discovery.
Data mining (sometimes called data or knowledge discovery) is also the process of analyzing data from
different perspectives and summarizing it into useful information - information that can be used to increase
revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data.
It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the
relationships identified. Technically, data mining is the process of finding correlations or patterns among
dozens of fields in large relational databases. The ultimate goal of data mining is prediction - and predictive
data mining is the most common type of data mining and one that has the most direct business applications.
The manual extraction of patterns from data has occurred for centuries. Early methods of identifying
patterns in data include Bayes’ theorem (1700s) and regression analysis (1800s). The increasing power of
computer technology has increased data collection, storage and manipulations. As data sets have grown in
size and complexity, direct hands-on data analysis has increasingly been augmented with indirect, automatic
data processing. This has been aided by other discoveries in computer science, such as neural networks,
clustering, genetic algorithms (1950s) and decision trees (1960s).
A primary reason for using data mining is to assist in the analysis of collections of observations of
behaviour. Such data are vulnerable to collinearity because of unknown interrelations. An unavoidable fact
of data mining is that the (sub-)set(s) of data being analysed may not be representative of the whole
domain, and therefore may not contain examples of certain critical relationships and behaviours that exist
across other parts of the domain. To address this sort of issue, the analysis may be augmented using
experiment-based and other approaches, such as Choice Modelling for human-generated data. In these
situations, inherent correlations can be either controlled for, or removed altogether, during the construction
of the experimental design.
Data mining techniques can be implemented rapidly on existing software and hardware platforms to
enhance the value of existing information resources, and can be integrated with new products and systems
as they are brought on-line.
There have been some efforts to define standards for data mining, for example the 1999 European
Cross Industry Standard Process for Data Mining (CRISP-DM 1.0) and the 2004 Java Data Mining standard
(JDM 1.0). These are evolving standards; later versions of these standards are under development.
FUNDAMENTALS OF DATA MINING
Data mining techniques are the result of a long process of research and product development. This
evolution began when business data was first stored on computers, continued with improvements in data
access, and more recently, generated technologies that allow users to navigate through their data in real
time. Data mining takes this evolutionary process beyond retrospective data access and navigation to
prospective and proactive information delivery. Data mining is ready for application in the business
community because it is supported by three technologies that are now sufficiently mature:
•
•
•
Massive data collection
Powerful multiprocessor computers
Data mining algorithms
57
Interaction of Computer and Statistics: A Case of Data Mining
In the evolution from business data to business information, each new step has built upon the previous
one. For example, dynamic data access is critical for drill-through in data navigation applications, and the
ability to store large databases is critical to data mining. From the user’s point of view, the four steps listed
below were revolutionary because they allowed new business questions to be answered precisely and
rapidly way.
Evolutionary Step
Business Question
Enabling Technologies Product Providers
Characteristics
Data Collection
(1960s)
“What was my total
revenue in the last
five years?”
Computers, tapes, disksIBM, CDC
Retrospective, static
data delivery
Relational databases
Retrospective,
Data Access (1980s) “What were unit sales
in India last March?”
Oracle, Sybase,
(RDBMS), Structured Informix, IBM,
Query Language (SQL), Microsoft
ODBC
dynamic data delivery
at record level
Data Warehousing
& Decision
Support (1990s)
“What were unit sales On-line analytic
in India last March?
processing (OLAP),
Drill down to Mumbai.” multidimensional
databases, data
warehouses
Pilot, Comshare,
Arbor, Cognos,
Microstrategy
Retrospective,
dynamic data delivery
at multiple levels
Data Mining
“What’s likely to
Advanced algorithms,
Pilot, Lockheed,
Prospective, proactive
(Emerging Today)
happen to Mumbai
unit sales next month?
Why?”
multiprocessor
computers, massive
databases
IBM, SGI, numerous information delivery
startups (nascent
industry)
The core components of data mining technology have been under development for decades, in
research areas such as statistics, artificial intelligence, and machine learning. Some crucial concepts of Data
Mining are:
Bagging (Voting, Averaging)
The concept of bagging (voting for classification, averaging for regression-type problems with
continuous dependent variables of interest) applies to the area of predictive data mining, to combine the
predicted classifications (prediction) from multiple models, or from the same type of model for different
learning data. It is also used to address the inherent instability of results when applying complex models to
relatively small data sets.
Boosting
The concept of boosting applies to the area of predictive data mining, to generate multiple models or
classifiers (for prediction or classification), and to derive weights to combine the predictions from those
models into a single prediction or predicted classification. Boosting will generate a sequence of classifiers,
where each consecutive classifier in the sequence is an “expert” in classifying observations that were not
well classified by those preceding it. Boosting can also be applied to learning methods that do not explicitly
support weights or misclassification costs.
Data Preparation
Data preparation and cleaning is an often neglected but extremely important step in the data mining
process. The old saying “garbage-in-garbage-out” is particularly applicable to the typical data mining
58
Gaurav Shukla, Pankaj Tiwari and Mohit Giri Goswami
projects where large data sets collected via some automatic methods (e.g., via the Web) serve as the input
into the analyses. Often, the method by which the data where gathered was not tightly controlled, and so
the data may contain out-of-range values (e.g., Income: -100), impossible data combinations (e.g., Gender:
Male, Pregnant: Yes
Data Reduction
The term Data Reduction in the context of data mining is usually applied to projects where the goal is
to aggregate or merge the information contained in large datasets into manageable (smaller) information
nuggets. Data reduction methods can include simple tabulation, aggregation (computing descriptive
statistics) or more sophisticated techniques like clustering, principal components analysis, etc.
Deployment
The concept of deployment in predictive data mining refers to the application of a model for prediction
or classification to new data. After a satisfactory model or set of models has been identified (trained) for a
particular application, we usually want to deploy those models so that predictions or predicted classifications
can quickly be obtained for new data. For example, a credit card company may want to deploy a trained
model or set of models (e.g., neural networks, meta-learner) to quickly identify transactions which have a
high probability of being fraudulent.
Drill-Down Analysis
The concept of drill-down analysis applies to the area of data mining, to denote the interactive
exploration of data, in particular of large databases. The process of drill-down analyses begins by
considering some simple break-downs of the data by a few variables of interest (e.g., Gender, geographic
region, etc.). Various statistics, tables, histograms, and other graphical summaries can be computed for
each group. Next, we may want to “drill-down” to expose and further analyze the data “underneath” one of
the categorizations.
Feature Selection
One of the preliminary stage in predictive data mining, when the data set includes more variables than
could be included (or would be efficient to include) in the actual model building phase (or even in initial
exploratory operations), is to select predictors from a large list of candidates. Feature selection selects a
subset of predictors from a large list of candidate predictors without assuming that the relationships
between the predictors and the dependent or outcome variables of interest are linear, or even monotone.
Therefore, this is used as a pre-processor for predictive data mining, to select manageable sets of predictors
that are likely related to the dependent (outcome) variables of interest, for further analyses with any of the
other methods for regression and classification.
Machine Learning
Machine learning, computational learning theory, and similar terms are often used in the context of
Data Mining, to denote the application of generic model-fitting or classification algorithms for predictive
data mining. Unlike traditional statistical data analysis, which is usually concerned with the estimation of
population parameters by statistical inference, the emphasis in data mining (and machine learning) is
usually on the accuracy of prediction (predicted classification), regardless of whether or not the “models”
or techniques that are used to generate the prediction is interpretable or open to simple explanation.
59
Interaction of Computer and Statistics: A Case of Data Mining
Meta-Learning
The concept of meta-learning applies to the area of predictive data mining, to combine the predictions
from multiple models. It is particularly useful when the types of models included in the project are very
different. In this context, this procedure is also referred to as Stacking (Stacked Generalization).
Models for Data Mining
In the business environment, complex data mining projects may require the coordinate efforts of
various experts, stakeholders, or departments throughout an entire organization. In the data mining literature,
various “general frameworks” have been proposed to serve as blueprints for how to organize the process
of gathering data, analyzing data, disseminating results, implementing results, and monitoring improvements.
General sequence of steps for data mining projects:
Business Understanding
Data Understanding
Modeling
Data Preparation
Modeling
Evaluation
Deployment
Another approach - the Six Sigma methodology - is a well-structured, data-driven methodology for
eliminating defects, waste, or quality control problems of all kinds in manufacturing, service delivery,
management, and other business activities. This model has recently become very popular (due to its
successful implementations). It postulated a sequence of, so-called, DMAIC steps
Define
Measure
Analyses
Improve
Control
that grew up from the manufacturing, quality improvement, and process control traditions and is
particularly well suited to production environments.
Another framework of this kind is the approach proposed by SAS Institute called SEMMA Sample
Explore
Modify
Model
Assess
which is focusing more on the technical activities typically involved in a data mining project.
Predictive Data Mining
The term Predictive Data Mining is usually applied to identify data mining projects with the goal to
identify a statistical or neural network model or set of models that can be used to predict some response of
interest. For example, a credit card company may want to engage in predictive data mining, to derive a
(trained) model or set of models (e.g., neural networks, meta-learner) that can quickly identify transactions
which have a high probability of being fraudulent.
Stacking (Stacked Generalization)
The concept of stacking (Stacked Generalization) applies to the area of predictive data mining, to
combine the predictions from multiple models. It is particularly useful when the types of models included in
the project are very different.
60
Gaurav Shukla, Pankaj Tiwari and Mohit Giri Goswami
Text Mining
While Data Mining is typically concerned with the detection of patterns in numeric data, very often
important (e.g., critical to business) information is stored in the form of text. Unlike numeric data, text is
often unstructured, and difficult to deal with. Text mining generally consists of the analysis of (multiple)
text documents by extracting key phrases, concepts, etc. and the preparation of the text processed in that
manner for further analyses with numeric data mining techniques (e.g., to determine co-occurrences of
concepts, key phrases, names, addresses, product names, etc.).
Architecture for Data Mining
Data Warehousing
Data warehousing as a process of organizing the storage of large, multivariate data sets in a way that
facilitates the retrieval of information for analytic purposes. The most efficient data warehousing architecture
will be capable of incorporating or at least referencing all data available in the relevant enterprise-wide
information management systems, using designated technology suitable for any data base management
(e.g., Oracle, Sybase, MS SQL Server).
To best apply these advanced techniques, they must be fully integrated with a data warehouse as well
as flexible interactive business analysis tools. Many data mining tools currently operate outside of the
warehouse, requiring extra steps for extracting, importing, and analyzing the data. The resulting analytic
data warehouse can be applied to improve business processes throughout the organization, in areas such
as promotional campaign management, fraud detection, new product rollout, and so on. The below figure
illustrates an architecture for advanced analysis in a large data warehouse.
Fig. 1
An OLAP (On-Line Analytical Processing) server enables a more sophisticated end-user business
model to be applied when navigating the data warehouse. The Data Mining Server must be integrated with
the data warehouse and the OLAP server to embed ROI-focused business analysis directly into this
infrastructure.
This design represents a fundamental shift from conventional decision support systems. Rather than
simply delivering data to the end user through query and reporting software, the Advanced Analysis
Server applies users’ business models directly to the warehouse and returns a proactive analysis of the
most relevant information. These results enhance the metadata in the OLAP Server by providing a dynamic
Interaction of Computer and Statistics: A Case of Data Mining
61
metadata layer that represents a distilled view of the data. Reporting, visualization, and other analysis tools
can then be applied to plan future actions and confirm the impact of those plans.
METHODS OF DATA MINING
Association Rule Learning
In data mining, association rule learning is a popular and well researched method for discovering
interesting relations between variables in large databases. Piatetsky-Shapiro describes analyzing and
presenting strong rules discovered in databases using different measures of interestingness. Based on the
concept of strong rules, Agrawal et al. introduced association rules for discovering regularities between
products in large scale transaction data recorded by point-of-sale (POS) systems in supermarkets.
For example, the rule {onions, potatoes}emplies {burger}found in the sales data of a supermarket
would indicate that if a customer buys onions and potatoes together, he or she is likely to also buy burger.
Such information can be used as the basis for decisions about marketing activities such as, e.g., promotional
pricing or product placements. In addition to the above example from market basket analysis association
rules are employed today in many application areas including Web usage mining, intrusion detection and
bioinformatics.
Cluster Analysis
Cluster Analysis is class of techniques use to classify objects or cases into relativily homogeneous
groups called clusters. Objects in each cluster tend to similiar to each other and dissimilar to object in the
other cluster. Cluster is also called classification analysis. Clustering is a method of unsupervised learning,
and a common technique for statistical data analysis used in many fields, including machine learning, data
mining, pattern recognition, and bioinformatics. Besides the term clustering, there are a number of terms
with similar meanings, including automatic classification, numerical taxonomy, botryology and typological
analysis.
Types of Clustering
Hierarchical Clustering: Hierarchical Clustering is characterized by development of hierarchy or tree
like structure. Hierarchical method can be agglomerative or divisive. Agglomerative clustering starts with
each object in separate cluster. Cluster are formed by grouping object into bigger and bigger cluster this
process is continue until all objects are members of a single cluster. Divisive clustering starts with all
objects grouped in a single cluster. Clusters are divided or split until each object is in separate cluster.
Agglomerative methods consists of linkage methods, error sum of squares or variance method and centroid
methods. Linkage method is a Agglomerative methods of Hierarchical Clustering that cluster objects based
on a computation of the distance between them. Variance method is a Agglomerative methods of Hierarchical
Clustering in which clusters are generated to minimize within cluster variance. Centriod method is a
Agglomerative methods of Hierarchical Clustering in which the distance between two clusters is the distance
between their centriods.
Non Hierarchical Clustering: The second type of clustering procedures, the Non Hierarchical
Clustering methods is frequently referred to as K-means clustering. These methods include sequential
threshold, parallel threshold and optimizing partitioning. Sequential threshold is Non Hierarchical Clustering
method in which a cluster center is selected and all objects with in a pre specified threshold value from the
center are grouped together. Similarly Parallel threshold is a Non Hierarchical Clustering method that
Gaurav Shukla, Pankaj Tiwari and Mohit Giri Goswami
62
specifies several cluster centers at once. All object that are within a pre specified threshold value from the
center are grouped together where as Optimizing partioning is Non Hierarchical Clustering method that
allows for later reassignment of objects to clusters to optimize and overall criterion.
Structured Data Analysis
Structured data analysis is the statistical data analysis of structured data. This can arise either in the
form of an a priori structure such as multiple-choice questionnaires or in situations with the need to search
for structure that fits the given data, either exactly or approximately. This structure can then be used for
making comparisons, predictions, manipulations etc. Regression analysis, Bayesian analysis, Combinational
Data analysis and Tree Structured Data analysis are important types structured data analysis.
Data Analysis
Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of
highlighting useful information, suggesting conclusions, and supporting decision making. Data analysis
has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different
business, science, and social science domains. Data mining is a particular data analysis technique that
focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes.
Business intelligence covers data analysis that relies heavily on aggregation, focusing on business
information. In statistical applications, some people divide data analysis into descriptive statistics, exploratory
data analysis, and confirmatory data analysis. EDA focuses on discovering new features in the data and
CDA on confirming or falsifying existing hypotheses.
Statistical Methods
A lot of statistical methods have been used for statistical data analyses. A very brief list of four of the
more popular methods is:
•
•
•
•
General linear model: A widely used model on which various statistical methods are based (e.g. t
test ANOVA ANCOVA MANOVA). Usable for assessing the effect of several predictors on one or
more continuous dependent variables.
Generalized linear model: An extension of the general linear model for discrete dependent variables.
Structural equation modelling: Usable for assessing latent structures from measured manifest
variables.
Item response theory: Models for (mostly) assessing one latent variable from several binary measured
variables.
Predictive Analytics
Predictive analytics is an area of statistical analysis that deals with extracting information from data
and using it to predict future trends and behaviour patterns. The core of predictive analytics relies on
capturing relationships between explanatory variables and the predicted variables from past occurrences,
and exploiting it to predict future outcomes. It is important to note, however, that the accuracy and
usability of results will depend greatly on the level of data analysis and the quality of assumptions.
Working of Data Mining
While large-scale information technology has been evolving separate transaction and analytical systems,
data mining provides the link between the two. Data mining software analyzes relationships and patterns in
Interaction of Computer and Statistics: A Case of Data Mining
63
stored transaction data based on open-ended user queries. Several types of analytical software are available:
statistical, machine learning, and neural networks. Generally, any of four types of relationships are sought:
•
Classes: Stored data is used to locate data in predetermined groups. For example, a restaurant
chain could mine customer purchase data to determine when customers visit and what they typically
order. This information could be used to increase traffic by having daily specials.
• Clusters: Data items are grouped according to logical relationships or consumer preferences. For
example, data can be mined to identify market segments or consumer affinities.
• Associations: Data can be mined to identify associations. The beer-diaper example is an example of
associative mining.
• Sequential patterns: Data is mined to anticipate behaviour patterns and trends. For example, an
outdoor equipment retailer could predict the likelihood of a backpack being purchased based on a
consumer’s purchase of sleeping bags and hiking shoes.
Data mining consists of five major elements:
• Extract, transform, and load transaction data onto the data warehouse system.
• Store and manage the data in a multidimensional database system.
• Provide data access to business analysts and information technology professionals.
• Analyze the data by application software.
• Present the data in a useful format, such as a graph or table.
Different levels of analysis are available:
•
•
•
•
•
•
Artificial neural networks: Non-linear predictive models that learn through training and resemble
biological neural networks in structure.
Genetic algorithms: Optimization techniques that use processes such as genetic combination,
mutation, and natural selection in a design based on the concepts of natural evolution.
Decision trees: Tree-shaped structures that represent sets of decisions. These decisions generate
rules for the classification of a dataset. Specific decision tree methods include Classification and
Regression Trees (CART) and Chi Square Automatic Interaction Detection (CHAID). CART and
CHAID are decision tree techniques used for classification of a dataset.
Nearest neighbor method: A technique that classifies each record in a dataset based on a
combination of the classes of the k record(s) most similar to it in a historical dataset (where k 1).
Sometimes called the k-nearest neighbor technique.
Rule induction: The extraction of useful if-then rules from data based on statistical significance.
Data visualization: The visual interpretation of complex relationships in multidimensional data.
Graphics tools are used to illustrate data relationships.
SCOPES AND APPLICATIONS
Scopes
Data mining derives its name from the similarities between searching for valuable business information
in a large database — for example, finding linked products in gigabytes of store scanner data — and
mining a mountain for a vein of valuable ore. Both processes require either sifting through an immense
amount of material, or intelligently probing it to find exactly where the value resides. Given databases of
sufficient size and quality, data mining technology can generate new business opportunities by providing
these capabilities:
Gaurav Shukla, Pankaj Tiwari and Mohit Giri Goswami
64
•
•
Automated prediction of trends and behaviours. Data mining automates the process of finding
predictive information in large databases.. A typical example of a predictive problem is targeted
marketing.
Automated discovery of previously unknown patterns. Data mining tools sweep through databases
and identify previously hidden patterns in one step. An example of pattern discovery is the analysis
of retail sales data to identify seemingly unrelated products that are often purchased together.
Other pattern discovery problems include detecting fraudulent credit card transactions and identifying
anomalous data that could represent data entry keying errors.
Applications
In recent years, data mining has been widely used in area of science and engineering, such as
bioinformatics, genetics, medicine, education and electrical power engineering.
In the area of study on human genetics, an important goal is to understand the mapping relationship
between the inter-individual variation in human DNA sequences and variability in disease susceptibility. In
lay terms, it is to find out how the changes in an individual’s DNA sequence affect the risk of developing
common diseases such as cancer. This is very important to help improve the diagnosis, prevention and
treatment of the diseases. The data mining technique that is used to perform this task is known as multifactor
dimensionality reduction.
In the area of electrical power engineering, data mining techniques have been widely used for condition
monitoring of high voltage electrical equipment. The purpose of condition monitoring is to obtain valuable
information on the insulation’s health status of the equipment. Data clustering such as self-organizing map
(SOM) has been applied on the vibration monitoring and analysis of transformer on-load tap-changers
(OLTCS). Using vibration monitoring, it can be observed that each tap change operation generates a signal
that contains information about the condition of the tap changer contacts and the drive mechanisms.
Obviously, different tap positions will generate different signals. However, there was considerable variability
amongst normal condition signals for exactly the same tap position. SOM has been applied to detect
abnormal conditions and to estimate the nature of the abnormalities.
Data mining techniques have also been applied for dissolved gas analysis (DGA) on power transformers.
DGA, as a diagnostics for power transformer, has been available for many years. Data mining techniques
such as SOM has been applied to analyse data and to determine trends which are not obvious to the
standard DGA ratio techniques such as Duval Triangle.
A fourth area of application for data mining in science/engineering is within educational research,
where data mining has been used to study the factors leading students to choose to engage in behaviours
which reduce their learning and to understand the factors influencing university student retention. A
similar example of the social application of data mining is its use in expertise finding systems, whereby
descriptors of human expertise are extracted, normalized and classified so as to facilitate the finding of
experts, particularly in scientific and technical fields. In this way, data mining can facilitate Institutional
memory.
Other examples of applying data mining technique applications are biomedical data facilitated by
domain ontologies, mining clinical trial data, traffic analysis using SOM, et cetera.
In adverse drug reaction surveillance, the Uppsala Monitoring Centre has, since 1998, used data
mining methods to routinely screen for reporting patterns indicative of emerging drug safety issues in the
Interaction of Computer and Statistics: A Case of Data Mining
65
WHO global database of 4.6 million suspected adverse drug reaction incidents. Recently, similar methodology
has been developed to mine large collections of electronic health records for temporal patterns associating
drug prescriptions to medical diagnoses.
CONCLUSION
A new technological leap is needed to structure and prioritize information for specific end-user problems
in engineering, medical agricultural and business fields. The data mining tools can make this leap.
Quantifiable business and others benefits have been proven through the integration of data mining with
current information systems, and new products are on the horizon that will bring this integration to an even
wider audience of users. The medical community sometimes uses data mining to help predict the
effectiveness of a procedure or medicine. Pharmaceutical firms use data mining of chemical compounds and
genetic material to help guide research on new treatments for diseases. Retailers can use information
collected through affinity programs. Recently, data mining has been increasingly cited as an important tool
for homeland security efforts. Some observers suggest that data mining should be used as a means to
identify terrorist activities, such as money transfers and communications.
REFERENCES
1.
Xingquan Zhu, Ian Davidson (2007). Knowledge Discovery and Data Mining: Challenges and Realities.
Hershey, New York. pp. 163–189.
2.
Pieter Adriaans and Dolf Zantinge, Data Mining (New York: Addison Wesley, 1996), pp.5-6.
3.
George Cahlink, “Data Mining Taps the Trends,” Government Executive Magazine, October 1, 2000,
4.
Y. Peng, G. Kou, Y. Shi, Z. Chen (2008). “A Descriptive Framework for the Field of Data Mining and
Knowledge Discovery”. International Journal of InformatioTecn hnology and Decision Making, Volume
7, Issue 4 7: 639 – 682.
5.
A.J. McGrail, E. Gulski et al.. “Data Mining Techniques to Asses the Condition of High Voltage Electrical
Plant”. CIGRE WG 15.11 of Study Committee 15.
6.
R. Baker. “Is Gaming the System State-or-Trait? Educational Data Mining Through the Multi-Contextual
Application of a Validated Behavioral Model”. Workshop on Data Mining for User Modeling 2007.