Introduction to Data Mining

Introduction to Data Mining 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 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. 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. 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. 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 1998 1999 From: R. Grossman, C. Kamath, V. Kumar, “Data Mining for Scientific and Engineering Applications” What is (not) Data Mining? is not Data Mining? – Look up phone number in phone directory  What  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 – Query a Web search engine for information about “Amazon” Origins of Data Mining • Draws ideas from: machine learning/AI, statistics, and database systems Statistics Machine Learning Data Mining Database systems 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 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. Learning We can think of at least three different problems being involved in learning: • memory, • averaging, and • generalization. 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. Memory • Okay. Let's say we observe our neighbor on three days: Temp 25 -5 Precip None Snow Day Sat Mon Shop No Yes Clothes Casual Casual Walk Drive 15 Snow Mon Yes Casual Walk 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 25 -5 15 -5 Precip None Snow Snow Snow Day Sat Mon Mon Mon Clothes Casual Casual Casual Casual Walk Drive Walk 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 25 -5 15 -5 Precip None Snow Snow Snow Day Sat Mon Mon Mon Clothes Casual Casual Casual Casual Walk Drive Walk Drive 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 25 25 25 25 25 25 25 25 Precip None None None None None None None None Day Sat Sat Sat Sat Sat Sat Sat Sat Clothes Casual Casual Casual Casual Casual Casual Casual Casual Walk Walk Drive Drive Walk Walk Walk ? • 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. 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 25 25 25 25 25 25 25 25 Precip None None None None None None None None Day Sat Sat Sat Sat Sat Sat Sat Sat Clothes Casual Casual Casual Casual Casual Casual Casual Casual Walk Walk Drive Drive Walk Walk Walk Walk Generalization • Dealing with previously unseen cases • Will she walk or drive? Temp 22 3 10 30 20 25 -5 27 24 Precip None None Rain None None None Snow None Rain Day Fri Sun Wed Mon Sat Sat Mon Tue Mon Clothes Casual Casual Casual Casual Formal Casual Casual Casual Casual Walk Walk Walk Drive Drive Drive Drive Drive ? • 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… Classification Another 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 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 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 Model Example of a Decision Tree 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 No Yes No Yes Splitting Attributes Yes No No Yes No No Yes No No No Single Married Single Married Refund Yes NO No MarSt Single, Divorced TaxInc < 80K NO > 80K YES Married NO Divorced 95K Married 60K Divorced 220K Single Married Single 85K 75K 90K Training Data Model: Decision Tree Apply Model to Test Data Test Data Start from the root of tree. Refund Marital Status No Married Taxable Income Cheat 80K ? Refund Yes NO No MarSt 10 Single, Divorced TaxInc < 80K NO > 80K YES Married NO Apply Model to Test Data Test Data Refund Marital Status No Married Taxable Income Cheat 80K ? Refund Yes NO No MarSt 10 Single, Divorced TaxInc < 80K NO > 80K YES Married NO Apply Model to Test Data Test Data Refund Marital Status No Married Taxable Income Cheat 80K ? Refund Yes NO No MarSt Single, Divorced TaxInc < 80K > 80K YES Married NO 10 NO Apply Model to Test Data Test Data Refund Marital Status No Married Taxable Income Cheat 80K ? Refund Yes NO No MarSt Single, Divorced TaxInc < 80K > 80K YES Married NO 10 NO Apply Model to Test Data Test Data Refund Marital Status No Married Taxable Income Cheat 80K ? Refund Yes NO No MarSt Single, Divorced TaxInc < 80K > 80K YES Married NO 10 NO Apply Model to Test Data Test Data Refund Marital Status No Married Taxable Income Cheat 80K ? Refund Yes NO No MarSt Single, Divorced TaxInc < 80K > 80K YES Married NO 10 Assign Cheat to “No” NO 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. 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. 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: Success story (Reported in 2003): • Verizon Wireless performed this kind of data mining • Use detailed record of transactions with reducing attrition rate from each of the past and present customers, to over 2% per month to under find attributes. E.g. 1.5% per month. – how often the customer calls, • Huge impact, with >30 M – where he calls, subscribers (0.5% is 150,000 – what time-of-the day he calls most, customers). – his financial status, – marital status, etc. • Label the customers as loyal or disloyal. Find a model for loyalty. 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. 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? 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. Beer and Diapers • What’s the explanation here? 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. 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. Genes • Baskets = people; items = genes or blood-chemistry factors. – Has been used to detect combinations of genes that result in diabetes 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 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. 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. 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. Each article is represented as a set of wordfrequency pairs (w, c).