2. Agenda
What is Data Mining?
Data Mining Tasks
Challenges in Data mining
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3. What is Data Mining
Data mining is integral part of knowledge
discovery in databases (KDD), which is the
overall process of converting raw data into
useful information. This process consists of
series of transformation steps from
preprocessing to postprocessing of data
mining results
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4. Process of Knowledge
Discovery in Database(KDD)
Data
Preprocessing
Data Mining PostProcessing
Normalization.
Data subsetting
Normalization.
Data subsetting
Filtering
Patterns,Visualization,
Pattern Interpretation
Filtering
Patterns,Visualization,
Pattern Interpretation
Inputdata
Input
Data Information
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5. Data Mining Tasks
Data Mining is generally divided into two
tasks.
1. Predictive tasks
2. Descriptive tasks
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6. Predictive Tasks
Objective: Predict the value of a specific
attribute (target/dependent variable)based
on the value of other attributes
(explanatory).
Example: Judge if a patient has specific
disease based on his/her medical tests results.
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7. Descriptive Tasks
Objective: To derive patterns
(correlation,trends,trajectories) that
summarizes the underlying relationship
between data.
Example: Identifying web pages that are
accessed together.(human interpretable
pattern)
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9. Classification: Definition
Classification: Given a collection of records
Each record contains a set of attributes, one of the
attribute is a class.
Find a model for class attribute as a function of
values of other attributes.
Goal: previously unseen records should be
assigned a class as accurately as possible.
A test set is used to determine the accuracy of the model.
Usually, the given data set is divided into training and
test sets, with training set used to build the model and
test set used to validate it.www.StudsPlanet.com
10. Classification: Example
Direct Marketing
Goal: Reduce cost of mailing by targeting a set of
consumers likely to buy a new product.
Approach:
Use the data for a similar product introduced before.
We know which customers decided to buy and which decided
otherwise. This {buy, don’t buy} decision forms the class
attribute.
Collect various demographic, lifestyle, and company-interaction
related information about all such customers.
Type of business, where they stay, how much they earn, etc.
Use this information as input attributes to learn a classifier
model. (from Berry & Linoff, 1997)
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11. Clustering: Definition
Given a set of data points, each having a set
of attributes, and a similarity measure among
them, find clusters such that
Data points in one cluster are more similar to one
another.
Data points in separate clusters are less similar to
one another.
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12. Clustering: Example
Document Clustering:
Goal: To find groups of documents that are similar to
each other based on the important terms appearing in
them.
Approach: To identify frequently occurring terms in
each document. Form a similarity measure based on the
frequencies of different terms. Use it to cluster.
Gain: Information Retrieval can utilize the clusters to
relate a new document or search term to clustered
documents.
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13. Illustrating Document Clustering
Category Total
Articles
Correctly Placed
Financial 555 364
Foreign 341 260
National 273 36
Metro 943 746
Sports 738 573
Entertainment 354 278
Clustering Points: 3204 Articles Of Los Angles Times.
Similarity Measure: How Many words are common in these
documents. (after some word filtering) (Introduction to Data mining 2007)
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14. Association Rule Discovery:
Definition
Given a set of records each of which contain some number of items
from a given collection;
Apriori principle: If an item set is frequent then its subset is also
frequent
TID Items
1 Bread, Coke Milk
2
3
Beer, Bread
Beer,Coke, Diaper, Milk
4 Beer, Bread, Diaper,
Milk
5 Coke, Diaper, Milk
Rule Discovered:
Milk -> Coke
Diaper, Milk -> Beer
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15. Other Mining Tasks in Nutshell
Sequential Pattern Discovery
In point-of-sale transaction sequences,
Computer Bookstore:
(Intro_To_Visual_C) (C++_Primer) -->
(Perl_for_dummies,Tcl_Tk)
Regression: Neural Networks
Deviation Detection: Detect deviation from normal
behavior. Eg. Credit card fraud.
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16. Challenges of Data Mining
Scalability
Dimensionality
Complex and Heterogeneous Data
Data Quality
Data Ownership and Distribution
Privacy Preservation
Streaming Data
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17. References
Tan, P., Steinbach, M., & Kumar, V.,
Introduction to Data Mining. Addison
Wesley, 2006.
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