Stats Overview Ji Zhu, Michigan Statistics 1. Overview. Ji Zhu 445C West Hall
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1 Stats Overview Ji Zhu, Michigan Statistics 1 Overview Ji Zhu 445C West Hall jizhu@umich.edu
2 Stats Overview Ji Zhu, Michigan Statistics 2 What is Data Mining? Data mining is a multi-disciplinary field of study concerned with the design of algorithms that allow computers to learn from large data repositories. Non-trivial extraction of implicit, previously unknown and potentially useful information from data There are many other definitions.
3 Stats Overview Ji Zhu, Michigan Statistics 3 Data Mining Examples and Non-Examples Data mining Certain names are more prevalent in certain US locations (O Brien, O Rurke, O Reilly... in Boston area) Group together similar documents returned by search engine according to their context (e.g. Amazon rainforest, Amazon.com, etc.) Not data mining Look up phone number in phone directory Query a web search engine for information about Amazon
4 Stats Overview Ji Zhu, Michigan Statistics 4
5 Stats Overview Ji Zhu, Michigan Statistics 5 Why Mine Data? Scientific Viewpoint Data collected and stored at enormous speeds (GB/hour) remote sensors on a satellite (NASA) telescopes scanning the skies (SDSS) microarrays generating gene expression data (MEDLINE) scientific simulations generating terabytes of data (GIS)
6 Stats Overview Ji Zhu, Michigan Statistics 6 Data mining may help scientists in classifying and segmenting data in hypothesis formation etc
7 Stats Overview Ji Zhu, Michigan Statistics 7 Why Mine Data? Commercial Viewpoint Lots of data is being collected and warehoused Web data, e-commerce (Google, Yahoo, Amazon, Ebay) Purchases at department and grocery stores (Walmart) Bank/credit card transactions (Bank of America, Visa, Mastercard)
8 Stats Overview Ji Zhu, Michigan Statistics 8 Competitive pressure is strong Provide better, customized services for an edge
9 Stats Overview Ji Zhu, Michigan Statistics 9 Mining Large Data Sets - Motivation There is often information hidden in the data that is not readily evident. Human analysts may take months to discover useful information. Much of the data is never analyzed at all.
10 Stats Overview Ji Zhu, Michigan Statistics 10 Technological Driving Factors Larger, cheaper memory Faster, cheaper processors the CRAY of 20 years ago is now on your desk Success of relational databases and the Web everybody is a data owner New ideas in machine learning and statistics
11 Stats Overview Ji Zhu, Michigan Statistics 11 Origins of Data Mining Draws ideas from machine learning, pattern recognition, statistics and database systems. Statistics Machine Learning/Pattern Recognition Data Mining Database Systems
12 Stats Overview Ji Zhu, Michigan Statistics 12 Traditional techniques may be unsuitable due to enormity of data high dimensionality of data heterogeneous, distributed nature of data
13 Stats Overview Ji Zhu, Michigan Statistics 13 Data Mining vs Statistics Traditional statistics first hypothesize, then collect data, then analyze often model-oriented Data mining few if any a priori hypothesis often algorithm-oriented rather than model-oriented
14 Stats Overview Ji Zhu, Michigan Statistics 14 Different? Yes, in terms of culture, motivation; however... Statistical ideas are very useful in data mining, e.g., in validating whether discovered knowledge is useful Increasing overlap at the boundary of statistics and data mining: use the tools of probability and statistics to provide a mathematical framework for posing data mining problems formulating solutions to those problems
15 Stats Overview Ji Zhu, Michigan Statistics 15 Data Mining vs Machine Learning To first-order, very little difference... Data mining relies heavily on ideas from machine learning (and from statistics) Some differences between data mining and machine learning More emphasis in data mining on scalability Data mining is somewhat more applications-oriented
16 Stats Overview Ji Zhu, Michigan Statistics 16 Two Types of 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
17 Stats Overview Ji Zhu, Michigan Statistics 17 Examples of Data Mining Tasks Visualization (Descriptive) Classification (Predictive) Regression (Predictive) Association analysis (Descriptive) Clustering (Descriptive)
18 Stats Overview Ji Zhu, Michigan Statistics 18 Classification: Definition Given a collection of data points (training set) Each data point contains a set of variables, one of the variables is the class Find a model for the class variable as a function of the values of other variables Goal: previously unseen data points should be assigned a class as accurately as possible A test set is used to determine the accuracy of the model
19 Stats Overview Ji Zhu, Michigan Statistics 19 Classification Example: Customer Scoring Example: a bank has a database of 1 million past customers, 10% of whom took out mortgages Use data mining to predict whether a new customer will take out a mortgage or not based on the customer data Customer data Other credit data Demographic data on the customer
20 Stats Overview Ji Zhu, Michigan Statistics 20 Classification Example: Spam Detection Customize an spam detection system for individual user. Relative frequencies in a message of most commonly occurring words and punctuation marks. george you your hp free re remove spam
21 Stats Overview Ji Zhu, Michigan Statistics 21 Classification Example: Microarray SID42354 SID31984 SID SIDW SID SID SIDW ESTsChr.10 SIDW SID SID SIDW SID SIDW ESTsChr.15 SIDW SIDW SIDW SID SIDW TUPLE1TUP1 ERLUMEN SIDW SID43609 ESTs SID52979 SIDW SIDW ESTs SMALLNUC SIDW ESTs SID SID SID ESTsChr.15 SID SIDW ESTsChr.2 SIDW SID46536 SIDW ESTsChr.5 SID SIDW ESTsChr.15 SIDW WASWiskott HYPOTHETIC SIDW SIDW SID SIDW HLACLASSI SIDW SIDW SIDW SID SID SID ESTsCh31 SIDW SIDW SIDW PTPRC SID SID ESTsChr.3 SID SID SIDW ESTsChr.6 SID47116 MITOCHOND Chr SIDW Homosapiens SIDW SIDW SID SIDW31489 SID DNAPOLYME SID ESTsChr.1 MYBPROTO SID ESTs SIDW HumanmRNA SIDW ESTs SID RASGTPASE SID H.sapiensmRN GNAL SID73161 SIDW SIDW BREAST RENAL MELANOMA MELANOMA MCF7D-repro COLON COLON K562B-repro COLON NSCLC LEUKEMIA RENAL MELANOMA BREAST CNS CNS RENAL MCF7A-repro NSCLC K562A-repro COLON CNS NSCLC NSCLC LEUKEMIA CNS OVARIAN BREAST LEUKEMIA MELANOMA MELANOMA OVARIAN OVARIAN NSCLC RENAL BREAST MELANOMA OVARIAN OVARIAN NSCLC RENAL BREAST MELANOMA LEUKEMIA COLON BREAST LEUKEMIA COLON CNS MELANOMA NSCLC PROSTATE NSCLC RENAL RENAL NSCLC RENAL LEUKEMIA OVARIAN PROSTATE COLON BREAST RENAL UNKNOWN
22 Stats Overview Ji Zhu, Michigan Statistics 22 Deviation/Anomaly Detection Detect significant deviations from normal behavior Applications: Credit card fraud detection Network intrusion detection
23 Stats Overview Ji Zhu, Michigan Statistics 23 Fraud Detection Credit card fraud detection Credit card losses in the US are over 1 billion $ per year Roughly 1 in 50k transactions are fraudulent Fair-Issac s fraud detection software based on neural networks, led to reported fraud decreases of 30 to 50% Issues: false alarm rate vs missed detection what is the tradeoff?
24 Stats Overview Ji Zhu, Michigan Statistics 24 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 Examples Predicting sales amounts of new product based on advertising expenditure Predicting wind velocities as a function of temperature, humidity, air pressure, etc Time series prediction of stock market indices
25 Stats Overview Ji Zhu, Michigan Statistics 25 Clustering: Definition Given a set of data points, each having a set of variables, find clusters such that data points in one cluster are more similar to one another, and data points in separate clusters are less similar to one another.
26 Stats Overview Ji Zhu, Michigan Statistics 26 Similarity measures Euclidean distance if variables are continuous Other problem-specific measures
27 Stats Overview Ji Zhu, Michigan Statistics 27 Clustering Example: 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. Collect different variables of customers based on their geographical and lifestyle related information Find clusters of similar customers
28 Stats Overview Ji Zhu, Michigan Statistics 28 Clustering Examples Document clustering: find groups of documents that are similar to each other based on the important terms appearing in them Clustering stocks based on their movements every day
29 Stats Overview Ji Zhu, Michigan Statistics 29 Association Rule Discovery: Definition Given a set of records each of which contains some number of items from a given collection Produce dependency rules which will predict occurrence of an item based on occurrences of other items Goal is to discover interesting local patterns in the data rather than to characterize the data globally
30 Stats Overview Ji Zhu, Michigan Statistics 30 ID Items 1 Bread, Coke, Milk 2 Beer, Bread 3 Beer, Coke, Diaper, Milk 4 Beer, Bread, Diaper, Milk 5 Coke, Diaper, Milk Rules discovered {Coke} = {Milk} {Diaper, Milk} = {Beer}
31 Stats Overview Ji Zhu, Michigan Statistics 31 Association Rule Discovery Example Supermarket shelf management Goal: To identify items that are bought together by sufficiently many customers A classic rule: If a customer buys diaper and milk, then he is very likely to buy beer Amazon, Netflix
32 Stats Overview Ji Zhu, Michigan Statistics 32 Hype? Data Mining: the Downside Data dredging, snooping and fishing Finding spurious structure in data that is not real Historically, data mining was derogatory term in the statistics community Rhine paradox The Super Bowl fallacy Bangladesh butter prices and the US stock market
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