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Data Mining
www.StudsPlanet.com
Agenda
 What is Data Mining?
 Data Mining Tasks
 Challenges in Data mining
www.StudsPlanet.com
What is Data Mining
 Data mining is integral part of knowledge
discovery in databases (KDD), which is the
overall process...
Process of Knowledge
Discovery in Database(KDD)
Data
Preprocessing
Data Mining PostProcessing
Normalization.
Data subsetti...
Data Mining Tasks
 Data Mining is generally divided into two
tasks.
1. Predictive tasks
2. Descriptive tasks
www.StudsPla...
Predictive Tasks
 Objective: Predict the value of a specific
attribute (target/dependent variable)based
on the value of o...
Descriptive Tasks
 Objective: To derive patterns
(correlation,trends,trajectories) that
summarizes the underlying relatio...
Data Mining Tasks [contd.]
 Classification [Predictive]
 Clustering [Descriptive]
 Association Rule Discovery[Descripti...
Classification: Definition
 Classification: Given a collection of records
 Each record contains a set of attributes, one...
Classification: Example
 Direct Marketing
Goal: Reduce cost of mailing by targeting a set of
consumers likely to buy a ne...
Clustering: Definition
 Given a set of data points, each having a set
of attributes, and a similarity measure among
them,...
Clustering: Example
 Document Clustering:
 Goal: To find groups of documents that are similar to
each other based on the...
Illustrating Document Clustering
Category Total
Articles
Correctly Placed
Financial 555 364
Foreign 341 260
National 273 3...
Association Rule Discovery:
Definition
Given a set of records each of which contain some number of items
from a given coll...
Other Mining Tasks in Nutshell
 Sequential Pattern Discovery
In point-of-sale transaction sequences,
 Computer Bookstore...
Challenges of Data Mining
 Scalability
 Dimensionality
 Complex and Heterogeneous Data
 Data Quality
 Data Ownership ...
References
 Tan, P., Steinbach, M., & Kumar, V.,
Introduction to Data Mining. Addison
Wesley, 2006.
www.StudsPlanet.com
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Data mining

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  • Transcript of "Data mining"

    1. 1. Data Mining www.StudsPlanet.com
    2. 2. Agenda  What is Data Mining?  Data Mining Tasks  Challenges in Data mining www.StudsPlanet.com
    3. 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 www.StudsPlanet.com
    4. 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 www.StudsPlanet.com
    5. 5. Data Mining Tasks  Data Mining is generally divided into two tasks. 1. Predictive tasks 2. Descriptive tasks www.StudsPlanet.com
    6. 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. www.StudsPlanet.com
    7. 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) www.StudsPlanet.com
    8. 8. Data Mining Tasks [contd.]  Classification [Predictive]  Clustering [Descriptive]  Association Rule Discovery[Descriptive]  Sequential Pattern Discovery [Descriptive]  Regression [Predictive]  Deviation Detection [Predictive] www.StudsPlanet.com
    9. 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. 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) www.StudsPlanet.com
    11. 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. www.StudsPlanet.com
    12. 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. www.StudsPlanet.com
    13. 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) www.StudsPlanet.com
    14. 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 www.StudsPlanet.com
    15. 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. www.StudsPlanet.com
    16. 16. Challenges of Data Mining  Scalability  Dimensionality  Complex and Heterogeneous Data  Data Quality  Data Ownership and Distribution  Privacy Preservation  Streaming Data www.StudsPlanet.com
    17. 17. References  Tan, P., Steinbach, M., & Kumar, V., Introduction to Data Mining. Addison Wesley, 2006. www.StudsPlanet.com
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