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An Introduction to Data Mining g BY:GAGAN DEEP KAUSHAL Trends leading g to Data Flood More data is generated: Bank, telecom, other business transactions ... Scientific Data: astronomy, biology, etc Web, text, and e-commerce More data is captured: Storage technology faster and cheaper DBMS capable of handling COM 307: Machine Learning and Data bigger DB Mining 2 Growth Trends Moore’s law Computer Speed doubles every 18 months Storage law total storage doubles every 9 months Consequence very little data will ever be looked at by a human h Data Mining is NEEDED to make sense and use of data data. COM 307: Machine Learning and Data Mining 3 Data Mining Definition Data mining in Data is the non-trivial process of identifying valid novel potentially useful and ultimately understandable patterns in data. COM 307: Machine Learning and Data Mining 4 What is Data Mining? z Many Definitions – Non-trivial o t a e extraction t act o o of implicit, p c t, p previously e ous y unknown u o 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 What is (not) Data Mining? What is not Data Mining? z z What is Data Mining? – Look up phone number in phone directory – Certain names are more prevalent in certain US S locations (O’Brien, O’Rurke, O’Reilly O Reilly… in Boston area) – Query a Web search engine for f information about Amazon “Amazon” – Group together similar documents returned by search engine according to their context (e (e.g. g Amazon rainforest, Amazon.com,) z Why Mine Data? Commercial Vi Viewpoint i t Lots of data is being collected and warehoused – Web data, e-commerce – purchases at department/ grocery stores – Bank/Credit Card t transactions ti z Computers p have become cheaper p and more p powerful z Competitive Pressure is Strong – Provide better better, customized services for an edge (e.g. (e g in Customer Relationship Management) Why Mine Data? Scientific Viewpoint z Data collected and stored at enormous speeds (GB/hour) – remote sensors on a satellite – telescopes scanning the skies – microarrays generating gene expression data – scientific simulations generating terabytes of data z z Traditional techniques infeasible for raw data D t mining Data i i may h help l scientists i ti t – in classifying and segmenting data i H th i F ti Related Fields Machine Learning Visualization Data Mining and Knowledge Discovery Statistics Databases COM 307: Machine Learning and Data Mining 9 Data Mining Process Integration Interpretation & Evaluation E l ti Knowledge Knowledge __ __ __ __ __ __ __ __ __ DATA Ware house Target Data Transformed Data COM 307: Machine Learning and Data Mining Patterns and Rules Understa anding Raw Data 10 Major Data Mining Tasks Classification: predicting an item class Associations: A i ti e.g. A & B & C occur ffrequently tl Visualization: to facilitate human discovery Estimation: predicting a continuous value Deviation Detection: finding changes Link Analysis: finding relationships … COM 307: Machine Learning and Data Mining 11 Classification: Definition z Given a collection of records (training set ) –E Each h record d contains t i a sett off attributes, tt ib t one off the th attributes is the class. Find a model for class attribute as a function of the values of other attributes. z Goal: previously unseen records should be assigned a class as accurately as possible. z – 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 d l and d ttestt sett used d tto validate lid t itit. Classification Example Tid R efund Refund M arital S tatus Taxable Incom e C heat Marital Status Taxable Incom e Cheat 1 Y es S ingle 125K No No Single 75K ? 2 No M arried 100K No Yes Married 50K ? 3 No S ingle 70K No No Married 150K ? 4 Y es M arried 120K No Yes Divorced 90K ? 5 No D ivorced i d 95K Y es N No Si l Single 40K ? 6 No M arried 60K No No Married 80K ? 10 7 Y es D ivorced 220K No 8 No S ingle 85K Y es 9 No M arried 75K No 10 10 No S ingle 90K Y es Training Set Learn Classifier Test Set Model Classification: Application 1 z Direct Marketing – Goal: G l Reduce R d costt off mailing ili b by targeting t ti a sett off 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 Classification: Application 2 z Fraud Detection – Goal: Predict fraudulent cases in credit card transactions transactions. – Approach: Use credit card transactions and the information on its accountholder 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. Association Rule Discovery: D fi iti Definition z Given a set of records each of which contain some number of items from a g given collection; – Produce dependency rules which will predict occurrence of an item based on occurrences of other items. TID Item s 1 2 3 4 5 Bread, C oke, M ilk B Beer, B Bread d Beer, C oke, D iaper, M ilk Beer, Bread, D iaper, M ilk C oke, k D iiaper, M ilk Rules Discovered: {Milk} --> {Coke} {Diaper, Milk} --> {Beer} Association Rule Discovery: Application 1 z Marketing and Sales Promotion: – Let L t the th rule l di discovered db be {Patty, … } --> {Potato Chips} – Potato Chips as consequent => Can be used to determine what should be done to boost its sales. – Patties in the antecedent => Can be used to see which products would be affected if the store discontinues selling patties. – Patty in antecedent and Potato chips in consequent => Can be used to see what products should be sold with patty to promote sale of Potato chips! Association Rule Discovery: Application 1 z Supermarket shelf management. – Goal: G l To T identify id tif items it that th t are bought b ht ttogether th by b sufficiently many customers. – Approach: A h P Process th the point-of-sale i t f l d data t collected with barcode scanners to find dependencies among items items. – A classic rule - If a customer buys diaper and milk, then he is very likely to buy beer. Association Rule Discovery: Application 2 z Inventory Management: – Goal: A consumer appliance repair company wants to anticipate the nature of repairs on its consumer products and keep the service vehicles equipped with right parts to reduce d on number b off visits i it tto consumer h households. h ld – Approach: Process the data on tools and parts required in previous repairs at different consumer locations and discover the co-occurrence patterns. z Sequential Pattern Discovery: D Definition fi iti Given is a set of objects, with each object associated with its own timeline of events, find rules that predict strong sequential dependencies among different events. events ((A B)) z (C) ( ) (D ( E)) Rules are formed by first disovering patterns. Event occurrences in the patterns are go governed erned b by timing constraints constraints. (A B) (C) (D E) z Sequential Pattern Discovery: Elogs, l Examples In telecommunications alarm – (Inverter_Problem Excessive_Line_Current) (Rectifier_Alarm) --> (Fire_Alarm) z In point-of-sale transaction sequences, – Computer C t Bookstore: B k t (Intro_To_Visual_C) (C++_Primer) --> (Perl_for_dummies,Tcl_Tk) – Athletic Apparel Store: (Shoes) (Racket, (Racket Racketball) --> > (Sports_Jacket) (Sports Jacket) Regression z z z Predict a value of a given continuous valued variable based on the values of other variables, assuming a linear or nonlinear model of dependency. Greatly studied in statistics, neural network fields. Examples: – Predicting sales amounts of new product based on advetising expenditure. – Predicting wind velocities as a function of temperature, humidity air pressure humidity, pressure, etc etc. – Time series prediction of stock market indices. Deviation/Anomaly Detection z Detect significant deviations from normal behavior z Applications: – Credit Card Fraud Detection – Network Intrusion Detection Typical network traffic at University level may reach over 100 million connections per day Challenges of Data Mining Scalability z Dimensionality z Co Complex pe a and d Heterogeneous e e oge eous Data aa z Data Quality z Data D t O Ownership hi and d Di Distribution t ib ti z Privacy Preservation z Streaming Data z ● . THANKS FOR YOUR KIND ATTENTION