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Data Mining: Introduction Lecture Notes for Chapter 1 Introduction to Data Mining, 2nd Edition by Tan, Steinbach, Karpatne, Kumar 01/17/2018 Introduction to Data Mining, 2nd Edition 1 Large-scale Data is Everywhere! ▪ ▪ There has been enormous data growth in both commercial and scientific da...

Data Mining: Introduction Lecture Notes for Chapter 1 Introduction to Data Mining, 2nd Edition by Tan, Steinbach, Karpatne, Kumar 01/17/2018 Introduction to Data Mining, 2nd Edition 1 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 (slogan) ▪ ▪ Cyber Security E-Commerce 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. Traffic Patterns Sensor Networks 01/17/2018 Introduction to Data Mining, 2nd Edition Social Networking: Twitter Computational Simulations 2 Why Data Mining? Commercial Viewpoint Lots of data is being collected and warehoused (stored) – Web data Yahoo has Peta Bytes of web data ◆ Facebook has billions of active users ◆ – purchases at department/ grocery stores, e-commerce ◆ Amazon handles millions of visits/day – 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) 01/17/2018 Introduction to Data Mining, 2nd Edition 3 Why Data Mining? Scientific Viewpoint Data collected and stored at enormous speeds – remote sensors on a satellite NASA EOSDIS archives over petabytes of earth science data / year ◆ fMRI Data from Brain Sky Survey Data – telescopes scanning the skies ◆ Sky survey data – High-throughput biological data – scientific simulations ◆ terabytes of data generated in a few hours Gene Expression Data Data mining helps scientists – in automated analysis of massive datasets – In hypothesis formation 01/17/2018 Introduction to Data Mining, 2nd Edition Surface Temperature of Earth 4 Great opportunities to improve productivity in all walks of life 01/17/2018 Introduction to Data Mining, 2nd Edition 5 Great Opportunities to Solve Society’s Major Problems Improving health care and reducing costs Predicting the impact of climate change Finding alternative/green energy sources Reducing hunger and poverty by increasing agriculture production 01/17/2018 Introduction to Data Mining, 2nd Edition 6 What is Data Mining? Many Definitions – Nontrivial extraction of implicit, previously unknown and potentially useful information from data – Exploration & analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns 01/17/2018 Introduction to Data Mining, 2nd Edition 7 What is (not) Data Mining? What is not Data Mining? What is Data Mining? – Look up phone number in phone directory – Certain names are more common in certain US locations (O’Brien, O’Rourke, O’Reilly… in Boston area) – Query a Web search engine for information about “Amazon” – Group together similar documents returned by search engine according to their context (e.g., Amazon rainforest, Amazon.com) 01/17/2018 Introduction to Data Mining, 2nd Edition 8 Origins of Data Mining Draws ideas from machine learning/AI, pattern recognition, statistics, and database systems Traditional techniques may be unsuitable due to data that is – Large-scale – High dimensional – Heterogeneous – Complex – Distributed A key component of the emerging field of data science and datadriven discovery 01/17/2018 Introduction to Data Mining, 2nd Edition 9 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. From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996 01/17/2018 Introduction to Data Mining, 2nd Edition 10 Data Mining Tasks … Data 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 No 7 Yes Divorced 220K No 8 No Single 85K Yes 9 No Married 75K No 10 No Single 90K Yes 11 No Married 60K No 12 Yes Divorced 220K No 13 No Single 85K Yes 14 No Married 75K No 15 No Single 90K Yes 60K 10 Milk 01/17/2018 Introduction to Data Mining, 2nd Edition 11 Predictive Modeling: Classification Find a model for class attribute as a function of the values of other attributes Model for predicting credit worthiness Class 1 Yes Graduate # years at present address 5 2 Yes High School 2 No 3 No Undergrad 1 No 4 Yes High School 10 Yes … … … … … Tid Employed Level of Education Employed Credit Worthy Yes No Yes No Education Graduate { High school, Undergrad } 10 Number of years 01/17/2018 Number of years > 3 yr < 3 yr > 7 yrs < 7 yrs Yes No Yes No Introduction to Data Mining, 2nd Edition 12 Classification Example 1 Yes Undergrad # years at present address 7 2 No Graduate 3 ? 3 Yes High School 2 ? … … … … … Tid Employed 1 Yes Graduate # years at present address 5 2 Yes High School 2 No 3 No Undergrad 1 No 4 Yes High School 10 Yes … … … … … Tid Employed Level of Education Credit Worthy Yes Level of Education ? 10 Test Set 10 Training Set 01/17/2018 Credit Worthy Learn Classifier Introduction to Data Mining, 2nd Edition Model 13 Examples of Classification Task Classifying credit card transactions as legitimate or fraudulent Classifying land covers (water bodies, urban areas, forests, etc.) using satellite data Categorizing news stories as finance, weather, entertainment, sports, etc. Identifying intruders in the cyberspace Predicting tumor cells as benign or malignant Classifying secondary structures of protein as alpha-helix, beta-sheet, or random coil 01/17/2018 Introduction to Data Mining, 2nd Edition 14 Classification: Application 1 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. 01/17/2018 Introduction to Data Mining, 2nd Edition 15 Classification: Application 2 Churn prediction for telephone customers – 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 timeof-the day he calls most, his financial status, marital status, etc. Label the customers as loyal or disloyal. ◆ Find a model for loyalty. ◆ From [Berry & Linoff] Data Mining Techniques, 1997 01/17/2018 Introduction to Data Mining, 2nd Edition 16 Classification: Application 3 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! From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996 01/17/2018 Introduction to Data Mining, 2nd Edition 17 Classifying Galaxies Courtesy: http://aps.umn.edu Early Class: • Stages of Formation Attributes: • Image features, • Characteristics of light waves received, etc. Intermediate Late Data Size: • 72 million stars, 20 million galaxies • Object Catalog: 9 GB • Image Database: 150 GB 01/17/2018 Introduction to Data Mining, 2nd Edition 18 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. Extensively studied in statistics, neural network fields. 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. 01/17/2018 Introduction to Data Mining, 2nd Edition 19 Clustering 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 Inter-cluster distances are maximized Intra-cluster distances are minimized 01/17/2018 Introduction to Data Mining, 2nd Edition 20 Applications of Cluster Analysis Understanding – Custom profiling for targeted marketing – Group related documents for browsing – Group genes and proteins that have similar functionality – Group stocks with similar price fluctuations Summarization – Reduce the size of large data sets Courtesy: Michael Eisen Clusters for Raw SST and Raw NPP 90 60 Land Cluster 2 latitude 30 Land Cluster 1 0 Ice or No NPP -30 Sea Cluster 2 -60 Use of K-means to partition Sea Surface Temperature (SST) and Net Primary Production (NPP) into clusters that reflect the Northern and Southern Hemispheres. Sea Cluster 1 -90 -180 -150 01/17/2018 -120 -90 -60 -30 0 30 longitude 60 90 120 150 180 Cluster Introduction to Data Mining, 2nd Edition 21 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. 01/17/2018 Introduction to Data Mining, 2nd Edition 22 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. Enron email dataset 01/17/2018 Introduction to Data Mining, 2nd Edition 23 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 2 3 4 5 Bread, Coke, Milk Beer, Bread Beer, Coke, Diaper, Milk Beer, Bread, Diaper, Milk Coke, Diaper, Milk 01/17/2018 Rules Discovered: {Milk} --> {Coke} {Diaper, Milk} --> {Beer} Introduction to Data Mining, 2nd Edition 24 Association Analysis: Applications Market-basket analysis – Rules are used for sales promotion, shelf management, and inventory management Telecommunication alarm diagnosis – Rules are used to find combination of alarms that occur together frequently in the same time period Medical Informatics – Rules are used to find combination of patient symptoms and test results associated with certain diseases 01/17/2018 Introduction to Data Mining, 2nd Edition 25 Association Analysis: Applications An Example Subspace Differential Coexpression Pattern Three lung cancer datasets [Bhattacharjee et a from lung cancer dataset 2001], [Stearman et al. 2005], [Su et al. 2007] Enriched with the TNF/NFB signaling pathway which is well-known to be related to lung cancer P-value: 1.4*10-5 (6/10 overlap with the pathway) [Fang et al PSB 2010] 01/17/2018 Introduction to Data Mining, 2nd Edition 26 Deviation/Anomaly/Change Detection Detect significant deviations from normal behavior Applications: – Credit Card Fraud Detection – Network Intrusion Detection – Identify abnormal behavior from sensor networks for monitoring and surveillance. – Detecting changes in the global forest cover. 01/17/2018 Introduction to Data Mining, 2nd Edition 27 Motivating Challenges Scalability High Dimensionality Heterogeneous and Complex Data Data Ownership and Distribution Non-traditional Analysis 01/17/2018 Introduction to Data Mining, 2nd Edition 28