Data Mining: Introduction
Introduction to Data Mining
(Tan, Steinbach and Kumar, 2006)
Data Mining: Introduction
Introduction to Data Mining
(Tan, Steinbach and Kumar, 2006)
Lots of data is being collected and warehoused
Web data, e-commerce
purchases
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)
Those who control information are the most powerful people on the planet (“El PSOE crea una base de datos que cubre toda España”)
Why Mine Data? Commercial Viewpoint
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
Traditional techniques infeasible for raw data
Data mining may help scientists
in classifying and segmenting data
in Hypothesis Formation
Mining Large Data Sets - Motivation 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 The Data Gap Total new disk (TB) since 1995 Number of analysts From: R. Grossman, C. Kamath, V. Kumar, “Data Mining for Scientific and Engineering Applications”
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 Knowledge Discovery in databases (KDD)
What is (not) Data Mining? What is Data Mining?
Group together similar documents returned by search engine according to their context (e.g. Amazon rainforest, Amazon.com,) What is not Data Mining?
Look up phone number in phone directory
Query a Web search engine for information about “Amazon”
Draws ideas from machine learning/AI, pattern recognition, statistics, and database systems
Motivating Challenges
Scalability
High dimensionality of data
Heterogeneous and complex data
Data ownership and Distribution
Non-traditional analysis Origins of Data Mining Machine Learning/
Pattern Recognition Statistics/ AI Data Mining Database systems
Data Mining Tasks Prediction Methods
Use some variables to predict unknown or future values of other variables.
Description Methods
Find human-interpretable patterns that describe the data.
From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996
Data Mining Tasks 2 Classification [Predictive]
Clustering [Descriptive]
Association Rule Discovery [Descriptive]
Regression [Predictive]
Deviation Detection [Predictive]
Classification: Definition Given a collection of records (training set )
Each record contains a set of attributes, one of the attributes is the class.
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.
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.
Classification Example categorical categorical continuous class Test
Set Training
Set Model Learn
Classifier
Classification: Application 1 (CRM) Direct Marketing
Goal: Reduce cost of mailing by targeting a set of consumers likely to buy a new cell-phone 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] Data Mining Techniques, 1997
Classification: Application 2 Fraud Detection
Goal: Predict fraudulent cases in credit card transactions.
Approach:
Use credit card transactions and the information on its account-holder as attributes.
When does a customer buy, what does he buy, how often he pays on time, etc
Label past transactions as fraud or fair transactions. This forms the class attribute.
Learn a model for the class of the transactions.
Use this model to detect fraud by observing credit card transactions on an account.
Classification: Application 3 (CRM) Customer Attrition/Churn:
Goal: To predict whether a customer is likely to be lost to a competitor.
Approach:
Use detailed record of transactions with each of the past and present customers, to find attributes.
How often the customer calls, where he calls, what time-of-the day he calls most, his financial status, marital status, etc.
Label the customers as loyal or disloyal.
Find a model for loyalty. From [Berry & Linoff] Data Mining Techniques, 1997
Classification: Application 4 Sky Survey Cataloging
Goal: To predict class (star or galaxy) of sky objects, especially visually faint ones, based on the telescopic survey images (from Palomar Observatory).
3000 images with 23,040 x 23,040 pixels per image.
Approach:
Segment the image.
Measure image attributes (features) - 40 of them per object.
Model the class based on these features.
Success Story: Could find 16 new high red-shift quasars, farthest objects that are difficult to find! From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996
Classifying Galaxies Early Intermediate Late Data Size:
72 million stars, 20 million galaxies
Object Catalog: 9 GB
Image Database: 150 GB Class:
Stages of Formation Attributes:
Image features,
Characteristics of light waves received, etc.
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.
Similarity Measures:
Euclidean Distance if attributes are continuous.
Other Problem-specific Measures.
Illustrating Clustering Euclidean Distance Based Clustering in 3-D space. Intracluster distances
are minimized Intercluster distances
are maximized
Clustering: Application 1 Market Segmentation:
Goal: subdivide a market into distinct subsets of customers where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix.
Approach:
Collect different attributes of customers based on their geographical and lifestyle related information.
Find clusters of similar customers.
Measure the clustering quality by observing buying patterns of customers in same cluster vs. those from different clusters.
Example (Vodafone segments in Spain: “Vodafone, Oro, Platino y Diamante”)
Clustering: Application 2 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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