Data Mining and Warehousing 20-`12-2007

Audience: III Year II sem engg. students + MCA II year Student
Jayaprakash narayan College of Engg. Mahboobnagar

Jpnce, 20-12-2007

CS05158: Data Warehousing and Mining


Lecture 1

• Course syllabus
• Overview of data warehousing and mining


Lecture slides modified from:
– Jiawei Han (http://www-sal.cs.uiuc.edu/~hanj/DM_Book.html)
– Vipin Kumar (http://www-users.cs.umn.edu/~kumar/csci5980/index.html)
– Ad Feelders (http://www.cs.uu.nl/docs/vakken/adm/)
– Zdravko Markov (http://www.cs.ccsu.edu/~markov/ccsu_courses/DataMining-1.html)
Rajesh Kulkarni
rrkpv2002@gmail.com
http://rkstechnofusion.blogspot.com
http://children-off-lesser-gods.blogspot.com

Course Syllabus
Textbook:
(required) J. Han, M. Kamber, Data Mining: Concepts and Techniques, 2001.
Data Mining Techniques by Arun K Pujari.
Datawarehousing in the Real World S Anahorey and D Murray
Topics: Unit 1
– Overview of data warehousing and mining
– Data Mining Functionalities
– Classification of Data Mining Systems
– Major Issues in Data Mining
– Data warehouse and OLAP technology for data mining
Motivation:
“Necessity is the Mother of Invention”
• Data explosion problem
– Automated data collection tools and mature database technology lead to tremendous amounts of data stored in databases, data warehouses and other information repositories
• We are drowning in data, but starving for knowledge!
• Solution: Data warehousing and data mining
– Data warehousing and on-line analytical processing
– Extraction of interesting knowledge (rules, regularities, patterns, constraints) from data in large databases
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)
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
What Is Data Mining?
• Data mining (knowledge discovery in databases):
– Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from data in large databases

• Alternative names and their “inside stories”:
– Data mining: a misnomer?
– Knowledge discovery(mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, business intelligence, etc.
Examples: What is (not) Data Mining?
Data Mining: Classification Schemes
• Decisions in data mining
– Kinds of databases to be mined
– Kinds of knowledge to be discovered
– Kinds of techniques utilized
– Kinds of applications adapted
• Data mining tasks
– Descriptive data mining
– Predictive data mining
Decisions in Data Mining
• Databases to be mined
– Relational, transactional, object-oriented, object-relational, active, spatial, time-series, text, multi-media, heterogeneous, legacy, WWW, etc.
• Knowledge to be mined
– Characterization, discrimination, association, classification, clustering, trend, deviation and outlier analysis, etc.
– Multiple/integrated functions and mining at multiple levels
• Techniques utilized
– Database-oriented, data warehouse (OLAP), machine learning, statistics, visualization, neural network, etc.
• Applications adapted
– Retail, telecommunication, banking, fraud analysis, DNA mining, stock market analysis, Web mining, Weblog analysis, etc.
Data Mining Tasks
• Prediction Tasks
– Use some variables to predict unknown or future values of other variables
• Description Tasks
– Find human-interpretable patterns that describe the data.

Common data mining tasks
– Classification [Predictive]
– Clustering [Descriptive]
– Association Rule Discovery [Descriptive]
– Sequential Pattern 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
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:
• 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.

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
• 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.

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, some of the farthest objects that are difficult to find!

Classifying Galaxies
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
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.

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.

Association Rule Discovery: Definition
• Given a set of records each of which contain some number of items from a given collection;
– Produce dependency rules which will predict occurrence of an item based on occurrences of other items.
Association Rule Discovery: Application 1
• Marketing and Sales Promotion:
– Let the rule discovered be
{Bagels, … } --> {Potato Chips}
– Potato Chips as consequent => Can be used to determine what should be done to boost its sales.
– Bagels in the antecedent => Can be used to see which products would be affected if the store discontinues selling bagels.
– Bagels in antecedent and Potato chips in consequent => Can be used to see what products should be sold with Bagels to promote sale of Potato chips!

Association Rule Discovery: Application 2
• Supermarket shelf management.
– Goal: To identify items that are bought together by sufficiently many customers.
– Approach: Process the point-of-sale data collected with barcode scanners to find dependencies among items.
– A classic rule --
• If a customer buys diaper and milk, then he is very likely to buy beer:
The Sad Truth About Diapers and Beer

• So, don’t be surprised if you find six-packs stacked next to diapers!


Sequential Pattern Discovery: Definition
Given is a set of objects, with each object associated with its own timeline of events, find rules that predict strong sequential dependencies among different events:

– In telecommunications alarm logs,
• (Inverter_Problem Excessive_Line_Current)
(Rectifier_Alarm) --> (Fire_Alarm)
– In point-of-sale transaction sequences,
• Computer Bookstore:
(Intro_To_Visual_C) (C++_Primer) --> (Perl_for_dummies,Tcl_Tk)
• Athletic Apparel Store:
(Shoes) (Racket, Racketball) --> (Sports_Jacket)
Regression
• Predict a value of a given continuous valued variable based on the values of other variables, assuming a linear or nonlinear model of dependency.
• Greatly studied in statistics, neural network fields.
• Examples:
– Predicting sales amounts of new product based on advetising expenditure.
– Predicting wind velocities as a function of temperature, humidity, air pressure, etc.
– Time series prediction of stock market indices.

Deviation/Anomaly Detection
• Detect significant deviations from normal behavior
• Applications:
– Credit Card Fraud Detection



– Network Intrusion
Detection

Data Mining and Induction Principle
Induction vs Deduction

• Deductive reasoning is truth-preserving:
– All horses are mammals
– All mammals have lungs
– Therefore, all horses have lungs

• Induction reasoning adds information:
– All horses observed so far have lungs.
– Therefore, all horses have lungs.

The Problems with Induction
From true facts, we may induce false models.

Prototypical example:
– European swans are all white.
– Induce: ”Swans are white” as a general rule.
– Discover Australia and black Swans...
– Problem: the set of examples is not random and representative

Another example: distinguish US tanks from Iraqi tanks
– Method: Database of pictures split in train set and test set; Classification model built on train set
– Result: Good predictive accuracy on test set;Bad score on independent pictures
– Why did it go wrong: other distinguishing features in the pictures (hangar versus desert)
Hypothesis-Based vs. Exploratory-Based
• The hypothesis-based method:
– Formulate a hypothesis of interest.
– Design an experiment that will yield data to test this hypothesis.
– Accept or reject hypothesis depending on the outcome.

• Exploratory-based method:
– Try to make sense of a bunch of data without an a priori hypothesis!
– The only prevention against false results is significance:
• ensure statistical significance (using train and test etc.)
• ensure domain significance (i.e., make sure that the results make sense to a domain expert)
Hypothesis-Based vs. Exploratory-Based
• Experimental Scientist:
– Assign level of fertilizer randomly to plot of land.
– Control for: quality of soil, amount of sunlight,...
– Compare mean yield of fertilized and unfertilized plots.

• Data Miner:
– Notices that the yield is somewhat higher under trees where birds roost.
– Conclusion: droppings increase yield.
– Alternative conclusion: moderate amount of shade increases yield.(“Identification Problem”)
Data Mining: A KDD Process
– Data mining: the core of knowledge discovery process.
Steps of a KDD Process
• Learning the application domain:
– relevant prior knowledge and goals of application
• Creating a target data set: data selection
• Data cleaning and preprocessing: (may take 60% of effort!)
• Data reduction and transformation:
– Find useful features, dimensionality/variable reduction, invariant representation.
• Choosing functions of data mining
– summarization, classification, regression, association, clustering.
• Choosing the mining algorithm(s)
• Data mining: search for patterns of interest
• Pattern evaluation and knowledge presentation
– visualization, transformation, removing redundant patterns, etc.
• Use of discovered knowledge
Data Mining and Business Intelligence
Data Mining: On What Kind of Data?
• Relational databases
• Data warehouses
• Transactional databases
• Advanced DB and information repositories
– Object-oriented and object-relational databases
– Spatial databases
– Time-series data and temporal data
– Text databases and multimedia databases
– Heterogeneous and legacy databases
– WWW
Data Mining: Confluence of Multiple Disciplines
Data Mining vs. Statistical Analysis
Statistical Analysis:
• Ill-suited for Nominal and Structured Data Types
• Completely data driven - incorporation of domain knowledge not possible
• Interpretation of results is difficult and daunting
• Requires expert user guidance

Data Mining:
• Large Data sets
• Efficiency of Algorithms is important
• Scalability of Algorithms is important
• Real World Data
• Lots of Missing Values
• Pre-existing data - not user generated
• Data not static - prone to updates
• Efficient methods for data retrieval available for use
Data Mining vs. DBMS
• Example DBMS Reports
– Last months sales for each service type
– Sales per service grouped by customer sex or age bracket
– List of customers who lapsed their policy

• Questions answered using Data Mining
– What characteristics do customers that lapse their policy have in common and how do they differ from customers who renew their policy?
– Which motor insurance policy holders would be potential customers for my House Content Insurance policy?

Data Mining and Data Warehousing
• Data Warehouse: a centralized data repository which can be queried for business benefit.
• Data Warehousing makes it possible to
– extract archived operational data
– overcome inconsistencies between different legacy data formats
– integrate data throughout an enterprise, regardless of location, format, or communication requirements
– incorporate additional or expert information
• OLAP: On-line Analytical Processing
• Multi-Dimensional Data Model (Data Cube)
• Operations:
– Roll-up
– Drill-down
– Slice and dice
– Rotate
DBMS, OLAP, and Data Mining
Example of DBMS, OLAP and Data Mining: Weather Data
Example of DBMS, OLAP and Data Mining: Weather Data
• By querying a DBMS containing the above table we may answer questions like:
• What was the temperature in the sunny days? {85, 80, 72, 69, 75}
• Which days the humidity was less than 75? {6, 7, 9, 11}
• Which days the temperature was greater than 70? {1, 2, 3, 8, 10, 11, 12, 13, 14}
• Which days the temperature was greater than 70 and the humidity was less than 75? The intersection of the above two: {11}
Example of DBMS, OLAP and Data Mining: Weather Data
OLAP:
• Using OLAP we can create a Multidimensional Model of our data (Data Cube).
• For example using the dimensions: time, outlook and play we can create the following model.
Major Issues in Data Warehousing and Mining
• Mining methodology and user interaction
– Mining different kinds of knowledge in databases
– Interactive mining of knowledge at multiple levels of abstraction
– Incorporation of background knowledge
– Data mining query languages and ad-hoc data mining
– Expression and visualization of data mining results
– Handling noise and incomplete data
– Pattern evaluation: the interestingness problem
• Performance and scalability
– Efficiency and scalability of data mining algorithms
– Parallel, distributed and incremental mining methods

Major Issues in Data Warehousing and Mining
• Issues relating to the diversity of data types
– Handling relational and complex types of data
– Mining information from heterogeneous databases and global information systems (WWW)
• Issues related to applications and social impacts
– Application of discovered knowledge
• Domain-specific data mining tools
• Intelligent query answering
• Process control and decision making
– Integration of the discovered knowledge with existing knowledge: A knowledge fusion problem
– Protection of data security, integrity, and privacy