Uploaded byDr. C.V. Suresh Babu
Data mining applications
Data mining is the process of analyzing large amounts of data to discover hidden patterns and relationships. It involves several steps including data preparation, modeling, evaluation, and deployment. A standard process like CRISP-DM provides guidelines and documentation to make the data mining process reliable and repeatable. Data mining can be used for applications like forecasting, classification, clustering, association analysis, and sequencing to help organizations in areas such as fraud detection, customer relationship management, and risk management.
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Data mining applications
- 1. What is DataMining?
- 2. Agenda • What DataMining IS and IS NOT • Steps in the Data Mining Process – CRISP-DM – Explanation of Models – Examples of Data Mining Applications • Questions
- 3. The Evolution ofData Analysis Evolutionary Step Business Question Enabling Product Providers Characteristics Technologies Data Collection "What was my total Computers, tapes, IBM, CDC Retrospective, (1960s) revenue in the last disks static data delivery five years?" Data Access "What were unit Relational Oracle, Sybase, Retrospective, (1980s) sales in New databases Informix, IBM, dynamic data England last (RDBMS), Microsoft delivery at record March?" Structured Query level Language (SQL), ODBC Data Warehousing "What were unit On-line analytic SPSS, Comshare, Retrospective, & Decision sales in New processing Arbor, Cognos, dynamic data Support England last (OLAP), Microstrategy,NCR delivery at multiple (1990s) March? Drill down multidimensional levels to Boston." databases, data warehouses Data Mining "What’s likely to Advanced SPSS/Clementine, Prospective, (Emerging Today) happen to Boston algorithms, Lockheed, IBM, proactive unit sales next multiprocessor SGI, SAS, NCR, information month? Why?" computers, massive Oracle, numerous delivery databases startups
- 4. Results of DataMining Include: • Forecasting what may happen in the future • Classifying people or things into groups by recognizing patterns • Clustering people or things into groups based on their attributes • Associating what events are likely to occur together • Sequencing what events are likely to lead to later events
- 5. Data mining isnot •Brute-force crunching of bulk data •“Blind” application of algorithms •Going to find relationships where none exist •Presenting data in different ways •A database intensive task •A difficult to understand technology requiring an advanced degree in computer science
- 6. Data Mining Is •A hot buzzword for a class of techniques that find patterns in data •A user-centric, interactive process which leverages analysis technologies and computing power •A group of techniques that find relationships that have not previously been discovered •Not reliant on an existing database •A relatively easy task that requires knowledge of the business problem/ subject matter expertise
- 7. Data Mining versus OLAP •OLAP - On-line Analytical Processing – Provides you with a very good view of what is happening, but can not predict what will happen in the future or why it is happening
- 8. Data Mining VersusStatistical Analysis •Data Mining •Data Analysis – Originally developed to act – Tests for statistical as expert systems to solve correctness of models problems • Are statistical – Less interested in the assumptions of models mechanics of the correct? technique – Eg Is the R-Square – If it makes sense then let’s good? use it – Hypothesis testing – Does not require • Is the relationship assumptions to be made significant? about data – Use a t-test to validate – Can find patterns in very significance large amounts of data – Tends to rely on sampling – Requires understanding – Techniques are not of data and business optimised for large amounts problem of data – Requires strong statistical skills
- 9. Examples of WhatPeople are Doing with Data Mining: •Fraud/Non-Compliance •Recruiting/Attracting Anomaly detection customers – Isolate the factors that •Maximizing lead to fraud, waste and profitability (cross selling, identifying abuse profitable customers) – Target auditing and •Service Delivery and investigative efforts more Customer Retention effectively – Build profiles of •Credit/Risk Scoring customers likely to use which •Intrusion detection services •Parts failure prediction •Web Mining
- 10. How Can WeDo Data Mining? By Utilizing the CRISP- DM Methodology – a standard process – existing data – software technologies – situational expertise
- 11. Why Should Therebe a Standard Process? •Framework for recording experience – Allows projects to be The data mining process must replicated be reliable and repeatable by •Aid to project planning and people with little data mining management •“Comfort factor” for new background. adopters – Demonstrates maturity of Data Mining – Reduces dependency on “stars”
- 12. Process Standardization CRISP-DM: • CRoss Industry Standard Process for Data Mining • Initiative launched Sept.1996 • SPSS/ISL, NCR, Daimler-Benz, OHRA • Funding from European commission • Over 200 members of the CRISP-DM SIG worldwide – DM Vendors - SPSS, NCR, IBM, SAS, SGI, Data Distilleries, Syllogic, Magnify, .. – System Suppliers / consultants - Cap Gemini, ICL Retail, Deloitte & Touche, … – End Users - BT, ABB, Lloyds Bank, AirTouch, Experian, ...
- 13. CRISP-DM •Non-proprietary •Application/Industry neutral •Tool neutral •Focus onbusiness issues – As well as technical analysis •Framework for guidance •Experience base – Templates for Analysis
- 14.
- 15. Why CRISP-DM? •The datamining process must be reliable and repeatable by people with little data mining skills •CRISP-DM provides a uniform framework for –guidelines –experience documentation •CRISP-DM is flexible to account for differences –Different business/agency problems –Different data
- 16. Phases and Tasks B u s in e s s D a ta D a ta M o d e lin g E v a lu a t io n D e p lo y m e n t U n d e r s t a n d in g U n d e r s t a n d in g P r e p a r a t io n D e t e r m in e C o lle c t In it ia l D a t a D ata Set S e le c t M o d e lin g E v a lu a t e R e s u lt s P la n D e p lo y m e n t B u s i n e s s O b j e c t Ii v e s D ata C ollection nitial D ata Set D escription T e c h n iq u e A ssessment of D ata D eployment P lan B ackground R eport M odeling T echnique M ining R esults w.r.t. B usiness Objectives S e le c t D a t a M odeling A ssumptions B usiness Success P la n M o n it o r in g a n d B usiness Success D e s c r ib e D a t a R ationale for I nclusion / C riteria M a in t e n a n c e C riteria D ata D escription R eport E xclusion G e n e r a t e T e s t D A pproved M odels e s ig n M onitoring and T est D esign M aintenance P lan S i t u a t i o n A s s e s s mEex p l o r e D a t a nt C le a n D a t a R e v ie w P r o c e s s I nventory of R esources D ata E xploration R eport D ata C leaning R eport B u i l d M o d e l R eview of P rocess P r o d u c e F in a l R e p o R equirements, P arameter Settings F inal R eport A ssumptions, and V e r i f y D a t a Q u a l i t y C o n s t r u c t D a tM odels a D e t e r m in e N e x t S F e p s resentation t inal P C onstraints D ata Q uality R eport D erived A ttributes M odel D escription List of P ossible A ctions R isks and C ontingencies Generated R ecords D ecision R e v ie w P r o je c t T erminology As s es s Model E xperience C osts and B enefits I n t e g r a t e D a t a odel A ssessment M D ocumentation M erged D ata R evised P arameter D e t e r m in e Settings D a t a M in in g G o a l F o rma t D a ta D ata M ining Goals R eformatted D ata D ata M ining Success C riteria P r o d u c e P r o je c t P la n P roj P lan ect I nitial A sessment of T ools and T echniques
- 17. Phases in theDM Process: CRISP-DM
- 18. Phases in theDM Process (1 & 2) •Business Understanding: – Statement of Business Objective – Statement of Data •Data Understanding Mining objective – Explore the data and – Statement of Success verify the quality Criteria – Find outliers
- 19. Phases in theDM Process (3) • Data preparation: – Takes usually over 90% of our time • Collection • Assessment • Consolidation and Cleaning – table links, aggregation level, missing values, etc • Data selection – active role in ignoring non- contributory data? – outliers? – Use of samples – visualization tools • Transformations - create new variables
- 20. Phases in theDM Process (4) • Model building – Selection of the modeling techniques is based upon the data mining objective – Modeling is an iterative process - different for supervised and unsupervised learning • May model for either description or prediction
- 21. Types of Models •PredictionModels for •Descriptive Models for Predicting and Grouping and Finding Classifying Associations – Regression algorithms – Clustering/Grouping (predict numeric outcome): neural algorithms: K- networks, rule means, Kohonen induction, CART (OLS – Association regression, GLM) algorithms: apriori, – Classification GRI algorithm predict symbolic outcome): CHAID, C5.0 (discriminant analysis, logistic regression)
- 22. Neural Network Input layer Hidden layer Output
- 23. Neural Networks • Description – Difficult interpretation – Tends to ‘overfit’ the data – Extensive amount of training time – A lot of data preparation – Works with all data types
- 24. Rule Induction •Description – Produces decision trees: • income < $40K – job > 5 yrs then good risk – job < 5 yrs then bad Credit ranking (1=default) risk Cat. % Bad 52.01 168 n Good 47.99 155 • income > $40K Total (100.00) 323 Paid Weekly/Monthly P-value=0.0000, Chi-square=179.6665, df=1 – high debt then bad risk Weekly pay Monthly salary – low debt then good risk Cat. % Bad 86.67 143 Good 13.33 22 n Cat. % Bad 15.82 25 Good 84.18 133 n Total (51.08) 165 Total (48.92) 158 – Or Rule Sets: Age Categorical P-value=0.0000, Chi-square=30.1113, df=1 Age Categorical P-value=0.0000, Chi-square=58.7255, df=1 • Rule #1 for good risk: Young (< 25);Middle (25-35) Cat. % n Old ( > 35) Cat. % n Cat. % Young (< 25) n Middle (25-35);Old ( > 35) Cat. % n – if income > $40K Bad 90.51 143 Good 9.49 15 Total (48.92) 158 Bad 0.00 Good 100.00 Total (2.17) 0 7 7 Bad 48.98 24 Good 51.02 25 Total (15.17) 49 Bad 0.92 1 Good 99.08 108 Total (33.75) 109 – if low debt Social Class P-value=0.0016, Chi-square=12.0388, df=1 • Rule #2 for good risk: Management;Clerical Cat. % n Professional Cat. % n – if income < $40K Bad 0.00 0 Bad 58.54 24 Good 100.00 8 Good 41.46 17 Total (2.48) 8 Total (12.69) 41 – if job > 5 years
- 25. Rule Induction Description • Intuitiveoutput • Handles all forms of numeric data, as well as non-numeric (symbolic) data C5 Algorithm a special case of rule induction • Target variable must be symbolic
- 26. Apriori Description • Seeks associationrules in dataset • ‘Market basket’ analysis • Sequence discovery
- 27. Kohonen Network Description • unsupervised •seeks to describe dataset in terms of natural clusters of cases
- 28. Phases in theDM Process (5) • Model Evaluation – Evaluation of model: how well it performed on test data – Methods and criteria depend on model type: • e.g., coincidence matrix with classification models, mean error rate with regression models – Interpretation of model: important or not, easy or hard depends on algorithm
- 29. Phases in theDM Process (6) •Deployment – Determine how the results need to be utilized – Who needs to use them? – How often do they need to be used •Deploy Data Mining results by: – Scoring a database – Utilizing results as business rules – interactive scoring on-line
- 30.
- 31. What data mininghas done for... The US Internal Revenue Service needed to improve customer service and... Scheduled its workforce to provide faster, more accurate answers to questions.
- 32. What data mininghas done for... The US Drug Enforcement Agency needed to be more effective in their drug “busts” and analyzed suspects’ cell phone usage to focus investigations.
- 33. What data mininghas done for... HSBC need to cross-sell more effectively by identifying profiles that would be interested in higher yielding investments and... Reduced direct mail costs by 30% while garnering 95% of the campaign’s revenue.
- 34. Final Comments •Data Mining can be utilized in any organization that needs to find patterns or relationships in their data. • By using the CRISP-DM methodology, analysts can have a reasonable level of assurance that their Data Mining efforts will render useful, repeatable, and valid results.
- 35.
Editor's Notes
- #32 The US Internal Revenue Service is using data mining to improve customer service. [Click] By analyzing incoming requests for help and information, the IRS hopes to schedule its workforce to provide faster, more accurate answers to questions.
- #33 The US DFAS needs to search through 2.5 million financial transactions that may indicate inaccurate charges. Instead of relying on tips to point out fraud, the DFAS is mining the data to identify suspicious transactions. [Click] Using Clementine, the agency examined credit card transactions and was able to identify purchases that did not match past patterns. Using this information, DFAS could focus investigations, finding fraud more costs effectively.
- #34 Retail banking is a highly competitive business. In addition to competition from other banks, banks also see intense competition from financial services companies of all kinds, from stockbrokers to mortgage companies. With so many organizations working the same customer base, the value of customer retention is greater than ever before. As a result, HSBC Bank USA looks to enticing existing customers to &quot;roll over&quot; maturing products, or on cross-selling new ones. [Click] Using SPSS products, HSBC found that it could reduce direct mail costs by 30% while still bringing in 95% of the campaign’s revenue. Because HSBC is sending out fewer mail pieces, customers are likely to be more loyal because they don’t receive junk mail from the bank.