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Data Mining: Introduction
Lecture Notes for Chapter 1 Introduction to Data Mining
by Tan, Steinbach, Kumar
© Tan,Steinbach, Kumar
Introduction to Data Mining
u Collect
various demographic, lifestyle, and company -interaction related information about all such customers.
– Type of business, where they stay, how much they earn, etc.
4/18/2004
‹#›
Why Mine Data? Commercial Viewpoint
●
Lots of data is being collected and warehoused – Web data, e-commerce – purchases at department/ grocery stores – Bank/Credit Card transactions Computers have become cheaper and more powerful Competitive Pressure is Strong – Provide better, customized services for an edge (e.g. in Customer Relationship Management)
Divorced 90K Single Married 40K 80K
Divorced 95K Married 60K
Divorced 220K Single Married Single 85K 75K 90K
No Yes No Yes
Test Set
Training Set
Learn Classifier
Find a model for class attribute as a function of the values of other attributes. ● Goal: previously unseen records should be assigned a class as accurately as possible.
● What
is Data Mining?
– Certain names are more prevalent in certain US locations (O’Brien, O’Rurke, O’Reilly… in Boston area)
© Tan,Steinbach, Kumar
– Group together similar documents returned by search engine according to their context (e.g. Amazon rainforest, Amazon.com,) Introduction to Data Mining 4/18/2004 ‹#›
●
4,000,000 3,500,000 3,000,000 2,500,000 2,000,000 1,500,000 1,000,000 500,000 0 1995 1996 1997
The Data Gap
Total new disk (TB) since 1995
Number of analysts
There is often information “hidden” in the data that is not readily evident ● Human analysts may take weeks to discover useful information ● Much of the data is never analyzed at all
Model
© Tan,Steinbach, Kumar
Iຫໍສະໝຸດ Baidutroduction to Data Mining
4/18/2004
‹#›
Classification: Application 1
●
Direct Marketing – Goal: Reduce cost of mailing by targeting a set of consumers likely to buy a new cell-phone product. – Approach:
Introduction to Data Mining 4/18/2004 ‹#›
● ●
© Tan,Steinbach, Kumar
Why Mine Data? Scientific Viewpoint
●
Data collected and stored at enormous speeds (GB/hour) – remote sensors on a satellite – telescopes scanning the skies – microarrays generating gene expression data – scientific simulations generating terabytes of data
© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
‹#›
Classification: Definition
●
Given a collection of records (training set )
– Each record contains a set of attributes, one of the attributes is the class.
u Use u We
the data for a similar product introduced before.
know which customers decided to buy and which decided otherwise. This {buy, don’t buy} decision forms the class attribute.
© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
‹#›
Classification Example
Tid Refund Marital Status 1 2 3 4 5 6 7 8 9 10
10
Taxable Income Cheat 125K 100K 70K 120K No No No No Yes No
What is Data Mining?
● Many
Definitions
– Non-trivial extraction of implicit, previously unknown and potentially useful information from data – Exploration & analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns
10
Refund Marital Status No Yes No Yes No No Single Married Married
Taxable Income Cheat 75K 50K 150K ? ? ? ? ? ?
Yes No No Yes No No Yes No No No
Single Married Single Married
●
– 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.
Data Mining Tasks...
● Classification [Predictive] ● Clustering [Descriptive] ● Association
Rule Discovery [Descriptive] ● Sequential Pattern Discovery [Descriptive] ● Regression [Predictive] ● Deviation Detection [Predictive]
Origins of Data Mining
● Draws
ideas from machine learning/AI, pattern recognition, statistics, and database systems ● Traditional Techniques may be unsuitable due to Statistics/ Machine Learning/ – Enormity of data AI Pattern Recognition – High dimensionality of data Data Mining – Heterogeneous, distributed nature Database systems of data
1998 1999
‹#›
© From: Tan,Steinbach, R. Grossman, Kumar C. Kamath, V. Kumar, Introduction “Data Mining to Data for Mining Scientific and Engineering Applications 4/18/2004 ”
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹#›
Data Mining Tasks
● Prediction
Methods – Use some variables to predict unknown or future values of other variables. Methods – Find human-interpretable patterns that describe the data.
● ●
Traditional techniques infeasible for raw data Data mining may help scientists – in classifying and segmenting data – in Hypothesis Formation
Mining Large Data Sets - Motivation
© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
‹#›
What is (not) Data Mining?
● What
is not Data Mining? – Look up phone number in phone directory – Query a Web search engine for information about “Amazon”
● Description
From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹#›
Lecture Notes for Chapter 1 Introduction to Data Mining
by Tan, Steinbach, Kumar
© Tan,Steinbach, Kumar
Introduction to Data Mining
u Collect
various demographic, lifestyle, and company -interaction related information about all such customers.
– Type of business, where they stay, how much they earn, etc.
4/18/2004
‹#›
Why Mine Data? Commercial Viewpoint
●
Lots of data is being collected and warehoused – Web data, e-commerce – purchases at department/ grocery stores – Bank/Credit Card transactions Computers have become cheaper and more powerful Competitive Pressure is Strong – Provide better, customized services for an edge (e.g. in Customer Relationship Management)
Divorced 90K Single Married 40K 80K
Divorced 95K Married 60K
Divorced 220K Single Married Single 85K 75K 90K
No Yes No Yes
Test Set
Training Set
Learn Classifier
Find a model for class attribute as a function of the values of other attributes. ● Goal: previously unseen records should be assigned a class as accurately as possible.
● What
is Data Mining?
– Certain names are more prevalent in certain US locations (O’Brien, O’Rurke, O’Reilly… in Boston area)
© Tan,Steinbach, Kumar
– Group together similar documents returned by search engine according to their context (e.g. Amazon rainforest, Amazon.com,) Introduction to Data Mining 4/18/2004 ‹#›
●
4,000,000 3,500,000 3,000,000 2,500,000 2,000,000 1,500,000 1,000,000 500,000 0 1995 1996 1997
The Data Gap
Total new disk (TB) since 1995
Number of analysts
There is often information “hidden” in the data that is not readily evident ● Human analysts may take weeks to discover useful information ● Much of the data is never analyzed at all
Model
© Tan,Steinbach, Kumar
Iຫໍສະໝຸດ Baidutroduction to Data Mining
4/18/2004
‹#›
Classification: Application 1
●
Direct Marketing – Goal: Reduce cost of mailing by targeting a set of consumers likely to buy a new cell-phone product. – Approach:
Introduction to Data Mining 4/18/2004 ‹#›
● ●
© Tan,Steinbach, Kumar
Why Mine Data? Scientific Viewpoint
●
Data collected and stored at enormous speeds (GB/hour) – remote sensors on a satellite – telescopes scanning the skies – microarrays generating gene expression data – scientific simulations generating terabytes of data
© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
‹#›
Classification: Definition
●
Given a collection of records (training set )
– Each record contains a set of attributes, one of the attributes is the class.
u Use u We
the data for a similar product introduced before.
know which customers decided to buy and which decided otherwise. This {buy, don’t buy} decision forms the class attribute.
© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
‹#›
Classification Example
Tid Refund Marital Status 1 2 3 4 5 6 7 8 9 10
10
Taxable Income Cheat 125K 100K 70K 120K No No No No Yes No
What is Data Mining?
● Many
Definitions
– Non-trivial extraction of implicit, previously unknown and potentially useful information from data – Exploration & analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns
10
Refund Marital Status No Yes No Yes No No Single Married Married
Taxable Income Cheat 75K 50K 150K ? ? ? ? ? ?
Yes No No Yes No No Yes No No No
Single Married Single Married
●
– 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.
Data Mining Tasks...
● Classification [Predictive] ● Clustering [Descriptive] ● Association
Rule Discovery [Descriptive] ● Sequential Pattern Discovery [Descriptive] ● Regression [Predictive] ● Deviation Detection [Predictive]
Origins of Data Mining
● Draws
ideas from machine learning/AI, pattern recognition, statistics, and database systems ● Traditional Techniques may be unsuitable due to Statistics/ Machine Learning/ – Enormity of data AI Pattern Recognition – High dimensionality of data Data Mining – Heterogeneous, distributed nature Database systems of data
1998 1999
‹#›
© From: Tan,Steinbach, R. Grossman, Kumar C. Kamath, V. Kumar, Introduction “Data Mining to Data for Mining Scientific and Engineering Applications 4/18/2004 ”
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹#›
Data Mining Tasks
● Prediction
Methods – Use some variables to predict unknown or future values of other variables. Methods – Find human-interpretable patterns that describe the data.
● ●
Traditional techniques infeasible for raw data Data mining may help scientists – in classifying and segmenting data – in Hypothesis Formation
Mining Large Data Sets - Motivation
© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
‹#›
What is (not) Data Mining?
● What
is not Data Mining? – Look up phone number in phone directory – Query a Web search engine for information about “Amazon”
● Description
From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹#›