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Introduction

Introduction

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  1. Introduction Introduction to Data Mining with Case Studies Author: G. K. Gupta Prentice Hall India, 2006.

  2. What is data mining? Why data mining? What applications? What techniques? What process? What software? Objectives ©GKGupta

  3. Definition Data mining may be defined as follows: data mining is a collection of techniques for efficient automated discovery of previously unknown, valid, novel, useful and understandable patterns in large databases. The patterns must be actionable so they may be used in an enterprise’s decision making. ©GKGupta

  4. What is Data Mining? • Efficient automated discovery of previously unknown patterns in large volumes of data. • Patterns must be valid, novel, useful and understandable. • Businesses are mostly interested in discovering past patterns to predict future behaviour. • A data warehouse, to be discussed later, can be an enterprise’s memory. Data mining can provide intelligence using that memory. ©GKGupta

  5. Examples • amazon.com uses associations. Recommendations to customers are based on past purchases and what other customers are purchasing. • A store in USA “Just for Feet” has about 200 stores, each carrying up to 6000 shoe styles, each style in several sizes. Data mining is used to find the right shoes to stock in the right store. • More examples in case studies to be discussed later. ©GKGupta

  6. Data Mining • We assume we are dealing with large data, perhaps Gigabytes, perhaps in Terabytes. • Although data mining is possible with smaller amount of data, bigger the data, higher the confidence in any unknown pattern that is discovered. • There is considerable hype about data mining at the present time and Gartner Group has listed data mining as one of the top ten technologies to watch. Question: How many books could one store in one Terabyte of memory? ©GKGupta

  7. Growth in generation and storage of corporate data – information explosion Need for sophisticated decision making – current database systems are Online Transaction Processing (OLTP) systems. The OLTP data is difficult to use for such applications. Why? Evolution of technology – much cheaper storage, easier data collection, better database management, to data analysis and understanding. Why Data Mining Now? ©GKGupta

  8. Database systems are being used since the 1960s in the Western countries (perhaps since 1980s in India). These systems have generated mountains of data. Point of sale terminals and bar codes on many products, railway bookings, educational institutions, huge number of mobile phones, electronic commerce, all generate data. Government is now collecting a lot of information. Information explosion ©GKGupta

  9. Internet banking via networked computers and ATMs. Credit and debit cards. Medical data, doctors, hospitals. Transportation, Indian railways, automatic toll collection on toll roads, growing air travel. Passports, NRI visas, Other visas, NRI money transfers. Question: Can you think of other examples of data collection? Information explosion ©GKGupta

  10. Many adults in India generate: Mobile phone transactions. More than 300 million phones in India, reportedly growing at the rate of 10,000 new ones every hour! Mobile companies must save information about calls. Growing middle class with growing number of credit and debit card transactions. About 25m credit cards and 70m debit cards in 2007. Annual growth rate about 30% and 40% respectively. Could be 55m credit cards and 200m debit cards in 2010 resulting in perhaps 500m transactions annually. Information explosion ©GKGupta

  11. India has some huge enterprises, for example Indian railways, perhaps the busiest network in the world with 2.5m employees, 10,000 locomotives, 10,000 passenger trains daily, 10,000 freight trains daily and 20m passengers daily. Growing airline traffic with more than ten airlines. Perhaps 30m passengers annually. Growing number of motor vehicles – registration, insurance, driver license Internet surfing records Information explosion ©GKGupta

  12. OLTP As noted earlier, most enterprise database systems were designed in the 1970’s or 1980’s and were mainly designed to automate some of the office procedures e.g. order entry, student enrolment, patient registration, airline reservations. These are well structured repetitive operations easily automated. ©GKGupta

  13. Need for business memory and intelligence. Need to serve customers better by learning from past interactions. OLTP data is not a good basis for maintaining an enterprise memory. The intelligence hidden in data could be the secret weapon in a competitive business world but given the information explosion not even a small fraction could be looked at by human eye. Question: Why OLTP is not good for maintaining an enterprise memory? Decision Making ©GKGupta

  14. OLTP vs Decision Making Clerical view of data focuses on details required for day-to-day running of an enterprise. Management view of data focuses on summary data to identify trends, challenges and opportunities. The detailed data view is the operational view while the management view is decision-support view. Comparison of the two views: ©GKGupta

  15. Operational vs Management View ©GKGupta

  16. Evolution of Technology • Corporate data growth accompanied by decline in the cost of storage and processing. • PC motherboard performance, measured in MHz/$, is currently doubling every 27 ± 2 months. • Next slide using logarithmic scale shows that disk is now about 10GB per US dollar and the following slide shows that sales of disk storage is growing exponentially. • Look at computing trends at http://www.zoology.ubc.ca/~rikblok/ComputingTrends/ Question: How much is the cost of 100GB disk? What is the cost of a PC and what is its CPU performance? ©GKGupta

  17. Decline in Hard Drive cost ©GKGupta

  18. Growth in Worldwide Disk Capacity ©GKGupta

  19. Evolution of Technology Question: What do the graphs in the last two slides tell us? What scales are used in them? What was the pink line is the first graph? ©GKGupta

  20. Evolution of Technology • Database technology has improved over the years. • Data collection is often much better and cheaper now • The need for analyzing and synthesizing information is growing in a fiercely competitive business environment of today. ©GKGupta

  21. New applications Sophisticated applications of modern enterprises include: - sales forecasting and analysis - marketing and promotion planning - business modeling OLTP is not designed for such applications. Also, large enterprises operate a number of database systems and then it is necessary to integrate information for decision making applications. Question: Why OLTP cannot be used for sales forecasting and analysis? ©GKGupta

  22. Why Data Mining Now? • As noted earlier, the reasons may be summarized as: • Accumulation of large amounts of data • Increased affordable computing power enabling data mining processing • Statistical and learning algorithms • Availability of software • Strong business competition ©GKGupta

  23. Large amount of data Already discussed that many enterprises have large amounts of data accumulated over 30+ years. Noted earlier that some enterprises collect information for analysis, for example, supermarkets in USA offer loyalty cards in exchange for shopper information. Loyalty cards in Australia also collect information using a reward system. ©GKGupta

  24. Growth of cards A recent survey in USA found that the percentages of US adults using the following types of cards were: • Credit cards - 88%; • ATM cards - 60% • Membership cards - 58% • Debit cards - 35% • Prepaid cards - 35% • Loyalty cards - 29% Question: What kind of data do these cards generate? ©GKGupta

  25. Affordable computing power Data mining is usually computationally intensive. Dramatic reduction in the price of computer systems, as noted earlier, is making it possible to carry out data mining without investing huge amounts of resources in hardware and software. In spite of affordable computing power, using data mining can be resources intensive. ©GKGupta

  26. Algorithms A variety of statistical and learning algorithms have been available in fields like statistics and artificial intelligence that have been adapted for data mining. With new focus on data mining, new algorithms are being developed. ©GKGupta

  27. Availability of Software Large variety of DM software is now available. Some more widely used software is: • IBM - Intelligent Miner and more • SAS - Enterprise Miner • Silicon Graphics - MineSet • Oracle - Thinking Machines - Darwin • Angoss - knowledgeSEEKER ©GKGupta

  28. Strong Business Competition Growth in service economies. Almost every business is a service business. Service economies are information rich and very competitive. Consider the telecommunications environment in Australia. About 20 years ago, Telstra was a monopoly. The field is now very competitive. Mobile phone market in India is also very competitive. ©GKGupta

  29. Applications In finance, telecom, insurance and retail: • Loan/credit card approval • market segmentation • fraud detection • better marketing • trend analysis • market basket analysis • customer churn • Web site design and promotion ©GKGupta

  30. In a modern society, a bank does not know its customers. Only knowledge a bank has is their information stored in the computer. Credit agencies and banks collect a lot of customers’ behavioural data from many sources. This information is used to predict the chances of a customer paying back a loan. Loan/Credit card approvals ©GKGupta

  31. Market Segmentation • Large amounts of data about customers contains valuable information • The market may be segmented into many subgroups according to variables that are good discriminators • Not always easy to find variables that will help in market segmentation ©GKGupta

  32. Fraud Detection • Very challenging since it is difficult to define characteristics of fraud. Often based on detecting changes from the norm. • In statistics, it is common to throw out the outliers but in data mining it may be useful to identify them since they could either be due to errors or perhaps fraud. ©GKGupta

  33. Better Marketing When customers buy new products, other products may be suggested to them when they are ready. As noted earlier, in mail order marketing for example, one wants to know: - will the customer respond? - will the customer buy and how much? - will the customer return purchase? - will the customer pay for the purchase? ©GKGupta

  34. Better Marketing It has been reported that more than 1000 variable values on each customer are held by some mail order marketing companies. The aim is to “lift” the response rate. ©GKGupta

  35. Trend analysis In a large company, not all trends are always visible to the management. It is then useful to use data mining software that will identify trends. Trends may be long term trends, cyclic trends or seasonal trends. ©GKGupta

  36. Market Basket Analysis • Aims to find what the customers buy and what they buy together • This may be useful in designing store layouts or in deciding which items to put on sale • Basket analysis can also be used for applications other than just analysing what items customers buy together ©GKGupta

  37. Customer Churn • In businesses like telecommunications, companies are trying very hard to keep their good customers and to perhaps persuade good customers of their competitors to switch to them. • In such an environment, businesses want to find which customers are good, why customers switch and what makes customers loyal. • Cheaper to develop a retention plan and retain an old customer than to bring in a new customer. ©GKGupta

  38. Customer Churn • The aim is to get to know the customers better so you will be able to keep them longer. • Given the competitive nature of businesses, customers will move if not looked after. • Also, some businesses may wish to get rid of customers that cost more than they are worth e.g. credit card holders that don’t use the card, bank customers with very small amount of money in their accounts. ©GKGupta

  39. Web site design • A Web site is effective only if the visitors easily find what they are looking for. • Data mining can help discover affinity of visitors to pages and the site layout may be modified based on this information. ©GKGupta

  40. Data Mining Process Successful data mining involves careful determining the aims and selecting appropriate data. The following steps should normally be followed: • Requirements analysis • Data selection and collection • Cleaning and preparing data • Data mining exploration and validation • Implementing, evaluating and monitoring • Results visualisation ©GKGupta

  41. Requirements Analysis The enterprise decision makers need to formulate goals that the data mining process is expected to achieve. The business problem must be clearly defined. One cannot use data mining without a good idea of what kind of outcomes the enterprise is looking for. If objectives have been clearly defined, it is easier to evaluate the results of the project. ©GKGupta

  42. Data Selection and Collection Find the best source databases for the data that is required. If the enterprise has implemented a data warehouse, then most of the data could be available there. Otherwise source OLTP systems need to be identified and required information extracted and stored in some temporary system. In some cases, only a sample of the data available may be required. ©GKGupta

  43. Cleaning and Preparing Data This may not be an onerous task if a data warehouse containing the required data exists, since most of this must have already been done when data was loaded in the warehouse. Otherwise this task can be very resource intensive, perhaps more than 50% of effort in a data mining project is spent on this step. Essentially a data store that integrates data from a number of databases may need to be created. When integrating data, one often encounters problems like identifying data, dealing with missing data, data conflicts and ambiguity. An ETL (extraction, transformation and loading) tool may be used to overcome these problems. ©GKGupta

  44. Exploration and Validation Assuming that the user has access to one or more data mining tools, a data mining model may be constructed based on the enterprise’s needs. It may be possible to take a sample of data and apply a number of relevant techniques. For each technique the results should be evaluated and their significance interpreted. This is likely to be an iterative process which should lead to selection of one or more techniques that are suitable for further exploration, testing and validation. ©GKGupta

  45. Implementing, Evaluating and Monitoring Once a model has been selected and validated, the model can be implemented for use by the decision makers. This may involve software development for generating reports or for results visualisation and explanation for managers. If more than one technique is available for the given data mining task, it is necessary to evaluate the results and choose the best. This may involve checking the accuracy and effectiveness of each technique. ©GKGupta

  46. Implementing, Evaluating and Monitoring Regular monitoring of the performance of the techniques that have been implemented is required. Every enterprise evolves with time and so must the data mining system. Monitoring may from time to time to lead to the refinement of tools and techniques that have been implemented. ©GKGupta

  47. Results Visualisation Explaining the results of data mining to the decision makers is an important step. Most DM software includes data visualisation modules which should be used in communicating data mining results to the managers. Clever data visualisation tools are being developed to display results that deal with more than two dimensions. The visualisation tools available should be tried and used if found effective for the given problem. ©GKGupta

  48. Data Mining Process – Another Approach The last few slides presented one approach. Another approach that also includes six steps has been proposed by CRISP–DM (Cross–Industry Standard Process for Data Mining) developed by an industry consortium. The six steps are: ©GKGupta

  49. CRISP–DM Steps The six CRISP–DM steps are: • Business understanding • Data understanding • Data preparation • Modelling • Evaluation • Deployment ©GKGupta

  50. CRISP–DM Steps The six steps proposed in CRISP–DM are similar to the six steps proposed earlier. . The CRIS–DM steps are shown in the following figure. Question: Compare the two sets of steps, one given in previous few slides and the CRISP-DM approach. Which approach is better? ©GKGupta