UNIT 2: Part 1: Data Warehousing and Data Mining
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This document provides an introduction to data mining and discusses key concepts such as why data is mined from both commercial and scientific viewpoints. It describes some of the largest databases in the world and different data mining tasks like classification, clustering, association rule learning etc. Specific applications of data mining discussed include direct marketing, fraud detection, credit risk assessment, customer churn prediction. The document also introduces concepts of predictive and descriptive data mining, supervised and unsupervised learning.
![Data Mining Tasks
Data mining tasks are generally divided into two major categories:
• Predictive tasks [Use some attributes to predict unknown or future
values of other attributes.]
• Classification
• Regression
• Deviation Detection
• Descriptive tasks [Find human-interpretable patterns that describe the
data.]
• Association Discovery
• Clustering](https://image.slidesharecdn.com/unit2-dwdmpart1-240227095506-d8e81360/85/UNIT-2-Part-1-Data-Warehousing-and-Data-Mining-11-320.jpg)
UNIT 2: Part 1: Data Warehousing and Data Mining
- 1. DATA WAREHOUSING AND DATA MINING UNIT – 2 Prepared by Mr. P. Nandakumar Assistant Professor, Department of IT, SVCET
- 2. Introduction to Data Mining
- 3. 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 • Twice as much information was created in 2002 as in 1999 (~30% growth rate) • Other growth rate estimates even higher
- 4. Largest databases in 2007 • Largest database in the world: World Data Centre for Climate (WDCC) operated by the Max Planck Institute and German Climate Computing Centre – 220 terabytes of data on climate research and climatic trends, – 110 terabytes worth of climate simulation data. – 6 petabytes worth of additional information stored on tapes. • AT&T – 323 terabytes of information – 1.9 trillion phone call records • Google – 91 million searches per day, • After a year worth of searches, this figure amounts to more than 33 trillion database entries.
- 5. Top 10 Largest Databases in the World • Library of Congress • Central Intelligence Agency • Amazon • YouTube • ChoicePoint • Sprint • Google • AT&T • National Energy Research Scientific Computing Center • World Data Centre for Climate Source: https://www.comparebusinessproducts.com/fyi/10- largest-databases-in-the-world
- 6. Why Mine Data? Scientific Viewpoint • Data is collected and stored at enormous speeds (GB/hour). E.g. – remote sensors on a satellite – telescopes scanning the skies – scientific simulations generating terabytes of data • Very little data will ever be looked at by a human • Knowledge Discovery is NEEDED to make sense and use of data.
- 7. Data Mining • Data mining is the process of automatically discovering useful information in large data repositories. • Human analysts may take weeks to discover useful information. • Much of the data is never analyzed at all. 0 500,000 1,000,000 1,500,000 2,000,000 2,500,000 3,000,000 3,500,000 4,000,000 1995 1996 1997 1998 1999 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”
- 8. What is (not) Data Mining? What is Data Mining? – Certain names are more prevalent in certain locations (O’Brien, O’Rurke, O’Reilly… in Boston area) –Discover groups of similar documents on the Web What is not Data Mining? – Look up phone number in phone directory – Query a Web search engine for information about “Amazon”
- 9. • Draws ideas from: machine learning/AI, statistics, and database systems Origins of Data Mining Machine Learning Statistics Data Mining Database systems
- 10. Data Mining Tasks Data mining involves six common classes of tasks: • Anomaly detection (Outlier/change/deviation detection) • Association rule learning (Dependency modelling) • Clustering • Classification • Regression • Summarization
- 11. Data Mining Tasks Data mining tasks are generally divided into two major categories: • Predictive tasks [Use some attributes to predict unknown or future values of other attributes.] • Classification • Regression • Deviation Detection • Descriptive tasks [Find human-interpretable patterns that describe the data.] • Association Discovery • Clustering
- 12. Architecture of Data Mining • Knowledge Base • Data Mining Engine • Pattern Evaluation Module • User interface
- 13. Classification of Data Mining Systems Data mining process: • State the problem and formulate the hypothesis • Collect the data
- 14. Major Issues In Data Mining 1. Mining different kinds of knowledge in databases 2. Interactive mining of knowledge at multiple levels of abstraction 3. Incorporation of background knowledge 4. Data mining query languages and ad hoc data mining 5. Presentation and visualization of data mining results 6. Handling noisy or incomplete data 7. Pattern evaluation 8. Efficiency and scalability of data mining algorithms 9. Parallel, distributed, and incremental mining algorithms
- 15. Predictive Data Mining or Supervised learning • Given a collection of records (training set) – Each record contains a set of attributes, one of the attributes is the class. • Find ("learn") a model for the class attribute as a function of the values of the other attributes. • Goal: previously unseen records should be assigned a class as accurately as possible.
- 16. Learning We can think of at least three different problems being involved in learning: • memory, • averaging, and • generalization.
- 17. Example problem (Adapted from Leslie Kaelbling's example in the MIT courseware) • Imagine that I'm trying predict whether my neighbor is going to drive into work, so I can ask for a ride. • Whether she drives into work seems to depend on the following attributes of the day: – temperature, – expected precipitation, – day of the week, – what she's wearing.
- 18. Memory • Okay. Let's say we observe our neighbor on three days: Clothes Shop Day Precip Temp Walk Casual No Sat None 25 Drive Casual Yes Mon Snow -5 Walk Casual Yes Mon Snow 15
- 19. Memory • Now, we find ourselves on a snowy “–5” – degree Monday, when the neighbor is wearing casual clothes and going shopping. • Do you think she's going to drive? Temp Precip Day Clothes 25 None Sat Casual Walk -5 Snow Mon Casual Drive 15 Snow Mon Casual Walk -5 Snow Mon Casual
- 20. Memory • The standard answer in this case is "yes". – This day is just like one of the ones we've seen before, and so it seems like a good bet to predict "yes." • This is about the most rudimentary form of learning, which is just to memorize the things you've seen before. Temp Precip Day Clothes 25 None Sat Casual Walk -5 Snow Mon Casual Drive 15 Snow Mon Casual Walk -5 Snow Mon Casual Drive
- 21. Noisy Data • Things aren’t always as easy as they were in the previous case. What if you get this set of noisy data? Temp Precip Day Clothes 25 None Sat Casual Walk 25 None Sat Casual Walk 25 None Sat Casual Drive 25 None Sat Casual Drive 25 None Sat Casual Walk 25 None Sat Casual Walk 25 None Sat Casual Walk 25 None Sat Casual ? • Now, we are asked to predict what's going to happen. • We have certainly seen this case before. • But the problem is that it has had different answers. Our neighbor is not entirely reliable.
- 22. Averaging • One strategy would be to predict the majority outcome. – The neighbor walked more times than she drove in this situation, so we might predict "walk". Temp Precip Day Clothes 25 None Sat Casual Walk 25 None Sat Casual Walk 25 None Sat Casual Drive 25 None Sat Casual Drive 25 None Sat Casual Walk 25 None Sat Casual Walk 25 None Sat Casual Walk 25 None Sat Casual Walk
- 23. Generalization • Dealing with previously unseen cases • Will she walk or drive? Temp Precip Day Clothes 22 None Fri Casual Walk 3 None Sun Casual Walk 10 Rain Wed Casual Walk 30 None Mon Casual Drive 20 None Sat Formal Drive 25 None Sat Casual Drive -5 Snow Mon Casual Drive 27 None Tue Casual Drive 24 Rain Mon Casual ? • We might plausibly make any of the following arguments: – She's going to walk because it's raining today and the only other time it rained, she walked. – She's going to drive because she has always driven on Mondays…
- 24. Classification Another Example Tid Refund Marital Status Taxable Income Cheat 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No 4 Yes Married 120K No 5 No Divorced 95K Yes 6 No Married 60K No 7 Yes Divorced 220K No 8 No Single 85K Yes 9 No Married 75K No 10 No Single 90K Yes 10 Refund Marital Status Taxable Income Cheat No Single 75K ? Yes Married 50K ? No Married 150K ? Yes Divorced 90K ? No Single 40K ? No Married 80K ? 10 Test Set Training Set Model Learn Classifier
- 25. Example of a Decision Tree Tid Refund Marital Status Taxable Income Cheat 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No 4 Yes Married 120K No 5 No Divorced 95K Yes 6 No Married 60K No 7 Yes Divorced 220K No 8 No Single 85K Yes 9 No Married 75K No 10 No Single 90K Yes 10 Refund MarSt TaxInc YES NO NO NO Yes No Married Single, Divorced < 80K > 80K Splitting Attributes Training Data Model: Decision Tree
- 26. Apply Model to Test Data Refund MarSt TaxInc YES NO NO NO Yes No Married Single, Divorced < 80K > 80K Refund Marital Status Taxable Income Cheat No Married 80K ? 10 Test Data Start from the root of tree.
- 27. Apply Model to Test Data Refund MarSt TaxInc YES NO NO NO Yes No Married Single, Divorced < 80K > 80K Refund Marital Status Taxable Income Cheat No Married 80K ? 10 Test Data
- 28. Apply Model to Test Data Refund MarSt TaxInc YES NO NO NO Yes No Married Single, Divorced < 80K > 80K Refund Marital Status Taxable Income Cheat No Married 80K ? 10 Test Data
- 29. Apply Model to Test Data Refund MarSt TaxInc YES NO NO NO Yes No Married Single, Divorced < 80K > 80K Refund Marital Status Taxable Income Cheat No Married 80K ? 10 Test Data
- 30. Apply Model to Test Data Refund MarSt TaxInc YES NO NO NO Yes No Married Single, Divorced < 80K > 80K Refund Marital Status Taxable Income Cheat No Married 80K ? 10 Test Data
- 31. Apply Model to Test Data Refund MarSt TaxInc YES NO NO NO Yes No Married Single, Divorced < 80K > 80K Refund Marital Status Taxable Income Cheat No Married 80K ? 10 Test Data Assign Cheat to “No”
- 32. Classification: 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 other related information about all such customers. E.g. • Type of business, • where they stay, • how much they earn, etc. • Use this information as input attributes to learn a classifier model.
- 33. Classification: Fraud Detection • Goal: Predict fraudulent cases in credit card transactions. • Approach: • Use credit card transactions and the information associated with them as attributes, e.g. – when does a customer buy, – what does he buy, – where does he buy, etc. • Label some 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.
- 34. Classification: Attrition/Churn • Situation: Attrition rate for mobile phone customers is around 25-30% a year! • 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. E.g. – 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. Success story (Reported in 2003): • Verizon Wireless performed this kind of data mining reducing attrition rate from over 2% per month to under 1.5% per month. • Huge impact, with >30 M subscribers (0.5% is 150,000 customers).
- 35. Assessing Credit Risk • Situation: Person applies for a loan • Task: Should a bank approve the loan? – People who have the best credit don’t need the loans – People with worst credit are not likely to repay. – Bank’s best customers are in the middle • Banks develop credit models using a variety of data mining methods. • Mortgage and credit card proliferation are the results of being able to "successfully" predict if a person is likely to default on a loan. • Widely deployed in many countries.
- 36. Frequent-Itemset Mining (Association Discovery) The Market-Basket Model • A large set of items, e.g., things sold in a supermarket. • A large set of baskets, each of which is a small set of the items, e.g., the things one customer buys on one day. Fundamental problem • What sets of items are often bought together? Application • If a large number of baskets contain both hot dogs and mustard, we can use this information in several ways. How?
- 37. Hot Dogs and Mustard 1. Apparently, many people walk from where the hot dogs are to where the mustard is. • We can put them close together, and put between them other foods that might also be bought with hot dogs and mustard, e.g., ketchup or potato chips. • Doing so can generate additional "impulse" sales. 2. The store can run a sale on hot dogs and at the same time raise the price of mustard. • People will come to the store for the cheap hot dogs, and many will need mustard too. • It is not worth the trouble to go to another store for cheaper mustard, so they buy that too. • The store makes back on mustard what it loses on hot dogs, and also gets more customers into the store.
- 38. On-Line Purchases • Amazon.com offers several million different items for sale, and has several tens of millions of customers. • Basket = Customer, Item = Book, DVD, etc. – Motivation: Find out what items are bought together. • Basket = Book, DVD, etc. Item = Customer – Motivation: Find out similar customers.
- 39. Words and Documents • Baskets = sentences; items = words in those sentences. – Lets us find words that appear together unusually frequently, i.e., linked concepts. • Baskets = sentences, items = documents containing those sentences. – Items that appear together too often could represent plagiarism.
- 40. Genes • Baskets = people; items = genes or blood-chemistry factors. – Has been used to detect combinations of genes that result in diabetes
- 41. Clustering • 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. E.g. Euclidean Distance Based Clustering in 3-D space. Intracluster distances are minimized Intercluster distances are maximized
- 42. 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.
- 43. Clustering: Application 2 • Document Clustering: – Goal: To find groups of documents that are similar to each other based on the important words appearing in them. – Approach: • Identify frequently occurring words 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 to clustered documents. Each article is represented as a set of word- frequency pairs (w, c). There are two natural clusters in the data set. The first cluster consists of the first four articles, which correspond to news about the economy. The second cluster contains the last four articles, which correspond to news about health care.