Digital Humanities Data Mining with Weka

Transcription

1 Digital Humanities Data Mining with Weka THATCamp Center for History and New Media, GMU Huzefa Rangwala Assistant Professor, Computer Science George Mason University Website: Slides are adapted from the available book slides developed by Tan, Steinbach and Kumar

2 Roadmap for Today Welcome & Introduction Introduction to Data Mining Examples, Motivation, Definition, Methods Classification Clustering Weka Iris Dataset Spam Dataset Your data (discussion)

3 What do you think of data mining? Please could you write down examples that you know of or have heard of on the provided index card. Also write down your own definition.

4 Data Mining Examples?

5 Large-scale Data is Everywhere! There has been enormous data growth in both commercial and scientific databases due to advances in data generation and collection technologies New mantra Gather whatever data you can whenever and wherever possible. Expectations Gathered data will have value either for the purpose collected or for a purpose not envisioned. Homeland Security Geo-spatial data Sensor Networks Business Data Computational Simulations

6 Why Data Mining? Commercial Viewpoint Lots of data is being collected and warehoused Web data Yahoo has 2PB web data Facebook has 800M? (1billion?) active users purchases at department/ grocery stores, e-commerce Amazon records 2M items/day Bank/Credit Card transactions Computers have become cheaper and more powerful

7 Why Data Mining? Scientific Viewpoint Data collected and stored at enormous speeds remote sensors on a satellite NASA EOSDIS archives over 1-petabytes of earth science data / year telescopes scanning the skies Sky survey data High-throughput biological data scientific simulations terabytes of data generated in a few hours Data mining helps scientists in automated analysis of massive datasets In hypothesis formation

8 Data Mining Example 1

9 My Favorite Data Mining Examples Amazon.com, Google, Netflix Personal Recommendations. Profile-based advertisements. Spam Filters/Priority Inbox Keep those efforts to pay us millions of dollars at bay. Scientific Discovery Grouping patterns in sky. Inferring complex life science processes. Forecasting weather. Security Phone Conversations, Network Traffic

10 Data Mining Definitions Non-trivial extraction of implicit, previously unknown and potentially useful information from data (normally large databases) Exploration & analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns. Part of the Knowledge Discovery in Databases Process.

11 What is (not) Data Mining? What is not Data Mining? Look up phone number in phone directory Query a Web search engine for information about Amazon What is 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,

12 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. Make good inferences from the data. From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996

13 Data Mining Tasks... Classification [Predictive] Clustering [Descriptive] Association Rule Discovery [Descriptive] Sequential Pattern Discovery [Descriptive] Regression [Predictive] Deviation Detection [Predictive]

14 10 10 Classification Example Tid Refund Marital Status Taxable Income Cheat 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 No Single 75K? Yes Married 50K? No Married 150K? Yes Divorced 90K? No Single 40K? No Married 80K? Test Set 9 No Married 75K No 10 No Single 90K Yes Training Set Learn Classifier Model

15 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.

16 Classification: Direct Marketing 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

17 Classification: Your Turn Historical Archival Mining Goal: Predict the era for a set of historical text documents available from National archives? Approach: What kind of data will you try to get? Can you say something about the characteristics of the data? Estimate the size of the data. What kind of pitfalls you might run into? You have 5 minutes to think and discuss.

18 Classifying Galaxies Courtesy: Early Class: Stages of Formation Intermediate Attributes: Image features, Characteristics of light waves received, etc. Late Data Size: 72 million stars, 20 million galaxies Object Catalog: 9 GB Image Database: 150 GB

19 What is Cluster Analysis? Finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups Intra-cluster distances are minimized Inter-cluster distances are maximized

20 Applications of Cluster Analysis Understanding Group related documents for browsing, group genes and proteins that have similar functionality, or group stocks with similar price fluctuations Discovered Clusters Applied-Matl-DOWN,Bay-Network-Down,3-COM-DOWN, Cabletron-Sys-DOWN,CISCO-DOWN,HP-DOWN, DSC-Comm-DOWN,INTEL-DOWN,LSI-Logic-DOWN, Micron-Tech-DOWN,Texas-Inst-Down,Tellabs-Inc-Down, Natl-Semiconduct-DOWN,Oracl-DOWN,SGI-DOWN, Sun-DOWN Apple-Comp-DOWN,Autodesk-DOWN,DEC-DOWN, ADV-Micro-Device-DOWN,Andrew-Corp-DOWN, Computer-Assoc-DOWN,Circuit-City-DOWN, Compaq-DOWN, EMC-Corp-DOWN, Gen-Inst-DOWN, Motorola-DOWN,Microsoft-DOWN,Scientific-Atl-DOWN Fannie-Mae-DOWN,Fed-Home-Loan-DOWN, MBNA-Corp-DOWN,Morgan-Stanley-DOWN Baker-Hughes-UP,Dresser-Inds-UP,Halliburton-HLD-UP, Louisiana-Land-UP,Phillips-Petro-UP,Unocal-UP, Schlumberger-UP Industry Group Technology1-DOWN Technology2-DOWN Financial-DOWN Oil-UP Summarization Reduce the size of large data sets Clustering precipitation in Australia

21 Clustering: Document 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.

22 Illustrating Document Clustering Clustering Points: 3204 Articles of Los Angeles Times. Similarity Measure: How many words are common in these documents (after some word filtering). Category Total Correctly Articles Placed Financial Foreign National Metro Sports Entertainment

23 Think point? Differences between classification and clustering?

24 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. TID Items 1 Bread, Coke, Milk 2 Beer, Bread 3 Beer, Coke, Diaper, Milk 4 Beer, Bread, Diaper, Milk 5 Coke, Diaper, Milk Rules Discovered: {Milk} --> {Coke}

25 Urban Legend. Classic Association Rule Example: If a customer buys diaper and milk, then he is very likely to buy beer. Any plausible explanations?

26 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!

27 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. Also called density estimation. Greatly studied in statistics, neural network fields. Examples: Predicting wind velocities as a function of temperature, humidity, air pressure, etc. Time series prediction of stock market indices.

28 Deviation/Anomaly Detection Detect significant deviations from normal behavior Applications: Credit Card Fraud Detection Network Intrusion Detection

29 What else can Data Mining do? Dilbert

30 Challenges of Data Mining Scalability Dimensionality Complex and Heterogeneous Data Data Quality Data Ownership and Distribution Privacy Preservation Streaming Data

31 Back to classification..

32 10 10 Illustrating Classification Task Tid Attrib1 Attrib2 Attrib3 Class 1 Yes Large 125K No 2 No Medium 100K No Learning algorithm 3 No Small 70K No 4 Yes Medium 120K No 5 No Large 95K Yes Induction 6 No Medium 60K No 7 Yes Large 220K No 8 No Small 85K Yes 9 No Medium 75K No Learn Model 10 No Small 90K Yes Training Set Tid Attrib1 Attrib2 Attrib3 Class Apply Model Model 11 No Small 55K? 12 Yes Medium 80K? 13 Yes Large 110K? Deduction 14 No Small 95K? 15 No Large 67K? Test Set

33 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.

34 Case Study: Sea Bass vs Salmon?

35 An Example Sorting incoming Fish on a conveyor according to species using optical sensing Species Sea bass Salmon

36 Problem Analysis Set up a camera and take some sample images to extract features Length Lightness Width Number and shape of fins Position of the mouth, etc This is the set of all suggested features to explore for use in our classifier!

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38 Classification Select the length of the fish as a possible feature for discrimination

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40 The length is a poor feature alone! Select the lightness as a possible feature.

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42 Adopt the lightness and add the width of the fish Fish x T = [x 1, x 2 ] Lightness Width

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44 Add more features: Produce an optimal decision boundary:

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46 However, our satisfaction is premature because the central aim of designing a classifier is to correctly classify novel input Issue of generalization!

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48 Move to WEKA. Spam Detection: Given an predict whether it is spam or not. Features? How will you measure accuracy?

49 How to best represent documents? Youn et. al. 2009

50 Metrics for Performance Evaluation Focus on the predictive capability of a model Rather than how fast it takes to classify or build models, scalability, etc. Confusion Matrix: ACTUA L CLASS PREDICTED CLASS Class=Yes Class=No Class=Yes a b Class=No c d a: TP (true positive) b: FN (false negative) c: FP (false positive) d: TN (true negative)

51 Not discussed (Classification) Several other classification evaluation metrics. Classification methods. Variants of binary classification.

52 Resources WEKA MALLET CLUTO UCI Machine Learning Repository (DataSets) Twitter.com/DMFun