1. 1 CISC 4631 Data Mining Lecture 01: Introduction to Data Mining

2. Let’s Start By Seeing What You Know Quick Quiz – Do you know what Data Mining is? – Do you know of any examples of Data Mining? 2

3. What is Data Mining? Data Mining has many definitions – Non-trivial 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 3

4. Alternative Names Data Mining was/is known by these other names (although many of these have lost favor over time): – Knowledge discovery in databases (KDD) – Knowledge extraction – Data/pattern analysis – Data archeology, data dredging, information harvesting, business intelligence, etc. Recently introduced new names (maybe with different emphases): – Data Science – Big Data 4

5. Some Examples Netflix and Amazon use data mining to recommend products (recommender systems) Companies use data mining for marketing – Who should be mailed a catalog – Who should see what online ads (Google Adwords) Fordham’s WISDM project uses smartphone accelerometer data to classify user activities (walking, jogging, sitting, etc.) Some search engines cluster retrieved documents into meaningful groups – Group pages about Jaguar into “car” pages and “cat” pages 5

6. Why Data Mining and Why Now? Data Mining was not very popular until about 10 – 15 years ago 6 Quick Quiz: What do you think changed?

7. Why Mine Data? There are now tremendous amounts of data that are automatically collected and warehoused. What are some examples? – Web data, e-commerce – Store purchases – Bank/Credit Card transactions – Cell phone GPS information – Smartphone and Smartwatch Sensor Data 7

8. Why Mine Data? What technological changes have helped make data mining so prevalent now? – Computers: cheaper and more powerful Smaller mobile devices are exploding in popularity – Disk and other storage: greater capacity and cheaper – Increased use of on-line resources and Internet We shouldn’t discount the advances in algorithms but most data mining algorithms are relatively mature 8

9. Why Mine Data? In business, competitive pressure is strong – Provide better, customized services for an edge (e.g. in Customer Relationship Management) – CRM is a relatively big deal now How do we get the most out of the customer over the long run Example: Customer Churn Analysis 9

10. Why Mine Data? Often info “ hidden ” in data is not evident Analysts may take weeks to discover useful information Much of the data is never analyzed at all – There is just too much data to analyze without “assistance” 10

11. Scientific Need Data collected at enormous speeds – remote sensors on satellite – telescopes scanning the skies – microarrays generating gene expression data – scientific simulations Traditional techniques infeasible 11

12. How Big is the Data? Examples of Large Data Sets – AT&T’s 26TB call detail database (2003) – Ebay 6PB, IRS 150TB data warehouse – Yahoo has a 2PB DB to analyze behavior of ½ billion web visitors/month (24 billion events/day) – Wal-Mart has a 583 TB database (2006) – Indexed web contains about 20 Billion pages – Sites like Facebook, Flicker & Twitter contain lots of data Google is estimated (in 2011) to have 900,000 servers to handle its data! 12

13. How Much Data is Being Created? 5 Exabytes new data created (2002, UC Berkeley) Humans created/copied 161/281 Exabytes in 06/07 (IDC) – 1 Exabyte = 10 18 – 12 stacks of books stretching from Earth to Sun – 3 million times the books ever written – Not all data stored at once ( includes temporary data) In 2012 2.8 ZB (2800EB) of data will be created/copied – Forecast for 2020: 40 ZB, or (57X number of grains of sand on Earth) 13 OK, we get the point already.! Head hurts.

14. Why Data Mining? Why Now? 14 According to BabyCenter.com, today one in three children born in the United States already have an online presence (usually in the form of a sonogram) before they are born. That number grows to 92% by the time they are two. In 2012 the average digital birth of children occurs at approximately six months, with a third of all children’s photos and information posted online within weeks of their birth. What will it mean to live in a world where our every moment, from birth to death, is digitally chronicled and preserved in vast cloud based databases, forever? During the first day of a baby’s life, the amount of data generated by humanity is equivalent to 70 times the information contained in the library of congress.

15. Origins of Data Mining Draws ideas from machine learning/AI, pattern recognition, statistics, and database systems* Traditional techniques may be unsuitable due to – Enormity of data – High dimensionality – Heterogeneous & distributed data * databases currently have limited impact; data mining is rarely done in a database but rather on “flat files” 15 Artificial Intelligence Machine Learning Pattern Recognition Statistics Data Mining Database systems

16. Statistics vs. Data Mining Experience has shown that students with statistics backgrounds are often confused by data mining if the differences aren’t highlighted When compared to Data Mining : Statistics is more theory-based – Data mining methods are often based on heuristic algorithms – Statistics is based firmly on mathematics (e.g., probability) Statistics is more focused on testing hypotheses vs. finding interesting relationships Statistics makes more assumptions about the data 16

17. The Process of Data Mining 17 Data Mining is a process, sometimes referred to as a knowledge discovery process. In this process there is a data mining step that applies data mining algorithms to extract knowledge. About 80% of our class will focus on the data mining step but in the real world 80% of the time is spent on the other steps (e.g., prepping data)

18. DATA MINING TASKS Second Part of Introduction: 18

19. Top-Level Data Mining Tasks At highest level, data mining tasks can be divided into: – Prediction Tasks (supervised learning) Use some variables to predict unknown or future values of other variables – Description Tasks (unsupervised learning) Find human-interpretable patterns that describe the data 19

20. Key Data Mining Tasks Overview of the major data mining tasks studied in this course: – Prediction Tasks Classification Regression – Description Tasks Clustering Association Rule Discovery 20

21. Classification: Definition Given a collection of records (training set ) – Each record contains a set of attributes, one of the attributes is the class, which is to be predicted. Find a model for class attribute as a function of the values of other attributes. – Model maps record to a class value Goal: previously unseen records should be assigned a class as accurately as possible. – A test set is used to determine accuracy of the model Can you think of classification tasks? 21

22. Classification Example categorical continuous class Test Set Training Set Model Learn Classifier

23. Classification: Application 1 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 info as input attributes to learn a classifier model 23

24. Classification: Application 2 Fraud Detection – Goal: Predict fraudulent cases in credit card transactions – Approach: Use credit card transactions and info on account-holders as attributes – When and what does customer buy, how often 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. 24

25. 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! 25 From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996

26. Classifying Galaxies 26 Early Intermediate Late Data Size: 72 million stars, 20 million galaxies Object Catalog: 9 GB Image Database: 150 GB Class: Stages of Formation Attributes: Image features, Characteristics of light waves received, etc. Courtesy: http://aps.umn.edu

27. Regression Predict a value of a given continuous (numerical) variable based on the values of other variables Greatly studied in statistics 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 27

28. Clustering Given a set of data points find clusters so that – Data points in same cluster are similar – Data points in different clusters are dissimilar 28 You try it on the Simpsons. How can we cluster these 5 “data points”?

29. 29 What is a natural grouping among these objects?

30. 30 School Employees Simpson's FamilyMalesFemales Clustering is subjective What is a natural grouping among these objects?

31. 31 What is Similarity? The quality or state of being similar; likeness; resemblance; as, a similarity of features. Similarity is hard to define, but… “We know it when we see it” The real meaning of similarity is a philosophical question. We will take a more pragmatic approach. Webster's Dictionary

32. Clustering: Application 1 Market Segmentation: – Goal: subdivide a market into distinct subsets of similar customers – 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. 32

33. Clustering: Application 2 Document Clustering: – Goal: Find groups of documents that are similar to each other based on the words appearing in them – Approach: Identify frequently occurring terms in each document. Form a similarity measure based on the frequencies of different terms. Use it to cluster. – Uses: Information Retrieval can utilize the clusters to relate a new document or search term to clustered documents. 33

34. Association Rule Discovery 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. 34 Rules Discovered: {Milk} --> {Coke} {Diaper, Milk} --> {Beer} Rules Discovered: {Milk} --> {Coke} {Diaper, Milk} --> {Beer} beer Diapers

35. Association Rule Discovery Application Marketing and Sales Promotion Applications – 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 => C an 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! Can help determine where to position store items – Supermarket shelf management – Did you ever notice that some stores have bananas in the cereal aisle? 35

36. Challenges of Data Mining Scalability Dimensionality Complex and Heterogeneous Data Data Quality Data Ownership and Distribution Privacy Preservation Streaming Data 36

37. What is (and is not) Data Mining? Based on the definitions of data mining, are these DM or not? – Finding a phone number in a directory Not data mining (trivial?, DB query) – Grouping related documents returned by search engine Is data mining (not trivial, clustering) – Identifying who has a disease based on symptoms Is data mining (not trivial, classification) – Web search on keyword using search engine May be data mining** ** More of an information retrieval task than data mining task. However, since Google does much more than keyword matching, there will be a data mining component. For example, Google mines the link structure of the Web to decide which pages are important (link mining is a type of data mining). 37

38. If you are Interested in Data Mining Data sets – NYC open data (https://nycopendata.socrata.com/)https://nycopendata.socrata.com/ – UCI Data Repository (http://archive.ics.uci.edu/ml/) Visit kdnuggets, an online newsletter and more – http://www.kdnuggets.com http://www.kdnuggets.com – You can arrange to have newsletter emailed to you – Also includes job openings ACM SIGKDD is the professional organization associated with data mining – ACM Special Interest Group (SIG) on data mining – Can join SIGKDD for $22 or for $54 can also join ACM as student member 38