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Data Mining
Knowledge on rough set theory
SUSHIL KUMAR SAHU
What is Data Mining??
Extraction of knowledge from data
exploration and analysis of large quantities of
data to discover meaningful pattern from data.
Discover Knowledge
Why datamining
Datamining is used in:
 pattern matching and restore the original
picture from a noisy one.
 Medical
 Business etc
 What datamining do:
 Finds relationship and make prediction.

Types of data mining
Relational data mining: It is the data mining
technique for relational databases. Unlike
traditional data mining algorithms, which
look for patterns in a single table , relational
data mining algorithms look for patterns
among multiple tables (relational patterns).
 Web mining: - is the application of data
mining techniques to discover patterns from
the Web.

Software Mining and Data Mining:
Instead of mining individual data sets,
software mining focuses on metadata, such
as database schemas. Knowledge Discovery
from software systems addresses structure,
behavior as well as the data processed by
the software system.
OLAP
OLAP deals with tools and technique for
data analysis that can give nearly
instantaneous answer to queries.
 OLAP use multidimensional array that
allow user to analyze the data.
 Datamining server must be integrated with
data warehouse and OLAP server.

Data Mining :
Motivation
Huge amounts of data
Important need for turning data into useful
information
Fast growing amount of data, collected and stored in
large and numerous databases exceeded the human
ability for comprehension without powerful tools
Data Mining Techniques
Decision Trees
Neural Network
Genetic Algorithms
Fuzzy Set Theory
Rough Set Theory
DATA MINING TECHNIQUES
Artificial neural networks: Non-linear
predictive models that learn through
training and resemble biological neural
networks in structure.
 Decision trees: Tree-shaped structures that
represent sets of decisions. These decisions
generate rules for the classification of a
dataset.

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Genetic algorithms: Optimization
techniques that use processes such as
genetic combination, mutation, and natural
selection in a design based on the concepts
of evolution.
THE ROUGH SET THEORY

One of the new data mining theories is the
rough set theory that can be used for
(1) Reduction of data sets
(2) Finding hidden data patterns
(3) Generation of decision rules
What is rough set
A rough set is a formal approximation of a
crisp set in terms of a pair of sets which
give the lower and the upper approximation
of the original set.
 The tuple
composed of the lower
and upper approximation is called a rough
set.The accuracy is perfect if αP(X) = 1

Reduct and Core
Reduct is a subset of attributes which by
itself can fully characterize the knowledge
in the database.
 The set of attributes which is common to all
reducts is called the core.

Data mining process
Stage-1
RAW DATA

Stage-2
K-MEANS
ALGORITHM
Stage-3
QUICK REDUCT
Stage-4
SYMBOLIC
RULES
Data preparation : Here data are prepared
from the database warehouse. Data is stored
using MATLAB.
 K-means algorithm: Data attribute obtained
from stage 1 is partitioned into k clusters
where each cluster comprises with datavectors with similar inherent characteristics

The K-Means Algorithm Process:
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The dataset is partitioned into K clusters and the data
points are randomly assigned to the clusters resulting in
clusters that have roughly the same number of data points .
For each data point, calculate the distance from the data
point to each cluster.
If the data point is closest to its own cluster leave it where
it is. If the data point is not closest to its own cluster, move
it into the closest cluster.
Repeat the above step until a complete pass through all the
data points results in no data point moving from one
cluster to another. At this point the clusters are stable and
the clustering process ends.

Quick-reduct algorithm:

Quick-reduct algorithm is used to compute a minimal
reduct without exhaustively generating all possible
subsets.

The reduction of attribute is achieved by
comparing equivalence relations generated by set
of attributes so that the reduced set provides the
same predictive capability of the decision feature
as the original.
QUICKREDUCT(C,D)

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C ->set of all conditional features;
D -> set of decision features.
(a) R ← {}
(b) Do
(c) T ← R
(d) ∀ x ∈ (C-R)
(e) if γ R∪ {x}(D) > γT(D)
where γR(D)=card(POSR(D)) / card(U)

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(f) T ← R∪{x}

(g) R ← T
(h) until γR(D) = = γC(D)

Rule extraction:

It uses the following Heuristic Approach
– Merge identical rows that has similar condition and
decision attribute
– Compute the core of every row
– Merge duplicate rows and compose a table with
reduct value
EXAMPLE

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Object
Weight
Door
Size
Cylinder
1
Low
2
Com
4
2
Low
4
Sub
6
3
Medium
4
Cum
4
4
High
2
Cum
6
5
High
4
Cum
4
6
Low
4
Cum
4
7
High
4
Sub
6
8
Low
2
Sub
6
Substitute LOW=1, MEDIUM=2, HIGH=3, COM=1 and SUB=2.
Applying K-Means clustering algorithm with K=2. The clustered rows
are {1, 3, 5, 6} and {2, 4, 7, 8}. Then the above table is reconstructed
using the clustered rows as the decision value, presented in Table 1.
Table-2

Data set after K-means clustering
Object
Weight
Door
Syze
Cylinder
Mileage
1
1
2
1
4
1
2
1
4
2
6
2
3
2
4
1
4
1
4
3
2
1
6
2
5
3
4
1
4
1
6
1
4
1
4
1
7
3
4
2
6
2
8
1
2
2
6
2
Applying the Quickreduct algorithm in Table 2, the final reduct
attributes {WEIGHT, DOOR, SIZE} is obtained. Hence, Table 2
can be reduced into Table 3 using the attribute reduct
{WEIGHT, DOOR, SIZE}.
Table-3
Attribute Reduction
Object
Weight
Door
Size
Mileage
1
1
2
1
1
2
1
4
2
2
3
2
4
1
1
4
3
2
1
2
5
3
4
1
1
6
1
4
1
1
7
3
4
2
2
8
1
2
2
2
Rule extraction

Merge identical objects of Table 3. otherwise compute the
core of every object in Table 3 and present it as in Table -4.
Object
Weight
Door
Size
Mileage
1
1
*
1
1
2
1
*
2
2
3
*
4
1
1
4
3
*
*
2
5
*
4
1
1
6
1
*
1
1
7
3
*
*
2
8
1
*
2
2

Merge duplicate objects with same decision value and
compose a table with the reduct value. That is, the merged
rows are {1, 6},{2, 8}, {3,5}and{4, 7}.

Merged table
Object
Weight
Door
Size
Mileage
1
1
*
1
1
2
1
*
2
2
3
*
4
1
1
4
3
*
*
2
The decision obtained from the above example
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Decision rules are often presented as implications
and are often called “if….then…” rules. We can
express the rules as follows:
If SIZE = 1 THEN MILEAGE = 1
If SIZE = 2 THEN MILEAGE = 2
If DOOR = 4 and SIZE = 1 THEN MILEAGE = 1
If WEIGHT = 3 THEN MILEAGE = 2
Classification of Data Mining Systems
Techniques
used
Application
adapted
DB oriented techniques
Statistic
Machine learning
Pattern recognition
Neural Network
Rough Set etc
Finance
Marketing
Medical
Stock
Telecommunication,
etc
Classification of Data Mining Systems
Kinds of DB
Kinds of Knowledge
Relational
Data warehouse
Transactional DB
Advanced DB system
Flat files
WWW
Classification
Association
Clustering
Prediction
…
…
Data Mining as a Step of KDD
Knowledge
Evaluation &
Presentation
Patterns
Data Mining
Selection and
Transformation
Cleaning and
Intergration
Databases
Data
Warehouse
Flat files
WHY MATLAB FOR DATA MINING?
As a programming language, MATLAB is
very like other procedural languages such as
Fortran or C.
 Graphing capability in MATLAB is among
the best in the business, and all MATLAB
graphs are compeltely configurable through
software.

Data Mining : Problems and Challenges
Noisy
data
Large
Databases
Dynamic
Databases
Incomplete
Data
Difficult
Training
Set
Performance Issues
Time and
Memory
Constrain
t
Predictive
Ability
Conclusion
Data Mining is an analytic process designed to explore data
in search of consistent patterns and/or systematic
relationships between variables, and then to validate the
findings by applying the detected patterns to new subsets
of data.
The ultimate goal of data mining is prediction.
Application of rough set theory in data mining is used for
time sequence analysis of electrical signal. It is also used in
medical diagnosis.
It is very effective due to its less time complexity, less cost ,
accuracy, cost of learning is less.
References
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www.google.com
www.icgst.com
http://en.wikipedia.org/wiki/Rough_set
http://en.wikipedia.org/wiki/Concept_mining
www.ieee.com
www.kurth.com
www.gosephtechnology.com
THANKS!!!
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UESTIONS?