Published on

1 Like
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide
  • September 98 1 Chapter Name
  • Ch35

    1. 1. Chapter 35 Data Mining Transparencies
    2. 2. Chapter Objectives The concepts associated with data mining. The main features of data mining operations, including predictive modeling, database segmentation, link analysis, and deviation detection. The techniques associated with the data mining operations. 2
    3. 3. Chapter Objectives The process of data mining. Important characteristics of data mining tools. The relationship between data mining and data warehousing. How Oracle supports data mining. 3
    4. 4. Data Mining The process of extracting valid, previously unknown, comprehensible, and actionable information from large databases and using it to make crucial business decisions, (Simoudis,1996). Involves the analysis of data and the use of software techniques for finding hidden and unexpected patterns and relationships in sets of data. 4
    5. 5. Data Mining Reveals information that is hidden and unexpected, as little value in finding patterns and relationships that are already intuitive. Patterns and relationships are identified by examining the underlying rules and features in the data. 5
    6. 6. Data Mining Tends to work from the data up and most accurate results normally require large volumes of data to deliver reliable conclusions. Starts by developing an optimal representation of structure of sample data, during which time knowledge is acquired and extended to larger sets of data. 6
    7. 7. Data Mining Data mining can provide huge paybacks for companies who have made a significant investment in data warehousing. Relatively new technology, however already used in a number of industries. 7
    8. 8. Examples of Applications of Data Mining Retail / Marketing – Identifying buying patterns of customers – Finding associations among customer demographic characteristics – Predicting response to mailing campaigns – Market basket analysis 8
    9. 9. Examples of Applications of Data Mining Banking – Detecting patterns of fraudulent credit card use – Identifying loyal customers – Predicting customers likely to change their credit card affiliation – Determining credit card spending by customer groups 9
    10. 10. Examples of Applications of Data Mining Insurance – Claims analysis – Predicting which customers will buy new policies Medicine – Characterizing patient behavior to predict surgery visits – Identifying successful medical therapies for different illnesses 10
    11. 11. Data Mining Operations Four main operations include: – Predictive modeling – Database segmentation – Link analysis – Deviation detection There are recognized associations between the applications and the corresponding operations. – e.g. Direct marketing strategies use database segmentation. 11
    12. 12. Data Mining Techniques Techniques are specific implementations of the data mining operations. Each operation has its own strengths and weaknesses. 12
    13. 13. Data Mining Techniques Data mining tools sometimes offer a choice of operations to implement a technique. Criteria for selection of tool includes – Suitability for certain input data types – Transparency of the mining output – Tolerance of missing variable values – Level of accuracy possible – Ability to handle large volumes of data 13
    14. 14. Data Mining Operations and AssociatedTechniques 14
    15. 15. Predictive Modeling Similar to the human learning experience – uses observations to form a model of the important characteristics of some phenomenon. Uses generalizations of ‘real world’ and ability to fit new data into a general framework. Can analyze a database to determine essential characteristics (model) about the data set. 15
    16. 16. Predictive Modeling Model is developed using a supervised learning approach, which has two phases: training and testing. – Training builds a model using a large sample of historical data called a training set. – Testing involves trying out the model on new, previously unseen data to determine its accuracy and physical performance characteristics. 16
    17. 17. Predictive Modeling Applications of predictive modeling include customer retention management, credit approval, cross selling, and direct marketing. There are two techniques associated with predictive modeling: classification and value prediction, which are distinguished by the nature of the variable being predicted. 17
    18. 18. Predictive Modeling - Classification Used to establish a specific predetermined class for each record in a database from a finite set of possible, class values. Two specializations of classification: tree induction and neural induction. 18
    19. 19. Example of Classification using Tree Induction 19
    20. 20. Example of Classification using NeuralInduction 20
    21. 21. Predictive Modeling - Value Prediction Used to estimate a continuous numeric value that is associated with a database record. Uses the traditional statistical techniques of linear regression and nonlinear regression. Relatively easy-to-use and understand. 21
    22. 22. Predictive Modeling - Value Prediction Linear regression attempts to fit a straight line through a plot of the data, such that the line is the best representation of the average of all observations at that point in the plot. Problem is that the technique only works well with linear data and is sensitive to the presence of outliers (that is, data values, which do not conform to the expected norm). 22
    23. 23. Predictive Modeling - Value Prediction Although nonlinear regression avoids the main problems of linear regression, it is still not flexible enough to handle all possible shapes of the data plot. Statistical measurements are fine for building linear models that describe predictable data points, however, most data is not linear in nature. 23
    24. 24. Predictive Modeling - Value Prediction Data mining requires statistical methods that can accommodate non-linearity, outliers, and non-numeric data. Applications of value prediction include credit card fraud detection or target mailing list identification. 24
    25. 25. Database Segmentation Aim is to partition a database into an unknown number of segments, or clusters, of similar records. Uses unsupervised learning to discover homogeneous sub-populations in a database to improve the accuracy of the profiles. 25
    26. 26. Database Segmentation Less precise than other operations thus less sensitive to redundant and irrelevant features. Sensitivity can be reduced by ignoring a subset of the attributes that describe each instance or by assigning a weighting factor to each variable. Applications of database segmentation include customer profiling, direct marketing, and cross selling. 26
    27. 27. Example of Database Segmentation using aScatterplot 27
    28. 28. Database Segmentation Associated with demographic or neural clustering techniques, which are distinguished by – Allowable data inputs – Methods used to calculate the distance between records – Presentation of the resulting segments for analysis 28
    29. 29. Link Analysis Aims to establish links (associations) between records, or sets of records, in a database. There are three specializations – Associations discovery – Sequential pattern discovery – Similar time sequence discovery Applications include product affinity analysis, direct marketing, and stock price movement. 29
    30. 30. Link Analysis - Associations Discovery Finds items that imply the presence of other items in the same event. Affinities between items are represented by association rules. – e.g. ‘When a customer rents property for more than 2 years and is more than 25 years old, in 40% of cases, the customer will buy a property. This association happens in 35% of all customers who rent properties’. 30
    31. 31. Link Analysis - Sequential Pattern Discovery Finds patterns between events such that the presence of one set of items is followed by another set of items in a database of events over a period of time. – e.g. Used to understand long term customer buying behavior. 31
    32. 32. Link Analysis - Similar Time SequenceDiscovery Finds links between two sets of data that are time-dependent, and is based on the degree of similarity between the patterns that both time series demonstrate. – e.g. Within three months of buying property, new home owners will purchase goods such as cookers, freezers, and washing machines. 32
    33. 33. Deviation Detection Relatively new operation in terms of commercially available data mining tools. Often a source of true discovery because it identifies outliers, which express deviation from some previously known expectation and norm. 33
    34. 34. Deviation Detection Can be performed using statistics and visualization techniques or as a by-product of data mining. Applications include fraud detection in the use of credit cards and insurance claims, quality control, and defects tracing. 34
    35. 35. Example of Database Segmentation using aVisualization 35
    36. 36. The Data Mining Process Recognizing that a systematic approach is essential to successful data mining, many vendor and consulting organizations have specified a process model designed to guide the user through a sequence of steps that will lead to good results. Developed a specification called the Cross Industry Standard Process for Data Mining (CRISP-DM). 36
    37. 37. The Data Mining Process CRISP-DM specifies a data mining process model that is not compliant with a particular industry or tool. CRISP-DM has evolved from the knowledge discovery processes used widely in industry and in direct response to user requirements. 37
    38. 38. The Data Mining Process The major aims of CRISP-DM are to make large data mining projects run more efficiently, be cheaper, more reliable, and more manageable. CRISP-DM is a hierarchical process model. At the top level, the process is divided into six different generic phases, ranging from business understanding to deployment of project results. 38
    39. 39. The Data Mining Process The next level elaborates each of these phases as comprising of several generic tasks. At this level, the description is generic enough to cover all the DM scenarios. The third level specialises these tasks for specific situations. For instance, the generic task might be cleaning data, and specialised task could be cleaning of numeric values or categorical values. 39
    40. 40. The Data Mining Process The fourth level is the process instance; that is a record of actions, decisions and result of an actual execution of DM project. The model also discusses relationships between different DM tasks. It gives idealised sequence of actions during a DM project. 40
    41. 41. Phases of the CRISP-DM Model 41
    42. 42. Data Mining Tools There are a growing number of commercial data mining tools on the marketplace. Important characteristics of data mining tools include: – Data preparation facilities – Selection of data mining operations – Product scalability and performance – Facilities for understanding results 42
    43. 43. Data Mining Tools Data preparation facilities – Data preparation is the most time- consuming aspect of data mining. – Functions supported include: data preparation, data cleansing, data describing, data transforming and data sampling. 43
    44. 44. Data Mining Tools Selection of data mining operations – Important to understand the characteristics of the operations (algorithms) to ensure that they meet the user’s requirements. – In particular, important to establish how the algorithms treat the data types of the response and predictor variables, how fast they train, and how fast they work on new data. 44
    45. 45. Data Mining Tools Product scalability and performance – Capable of dealing with increasing amounts of data, possibly with sophisticated validation controls. – Maintaining satisfactory performance may require investigations into whether a tool is capable of supporting parallel processing using technologies such as SMP or MPP. 45
    46. 46. Data Mining Tools Facilities for understanding results – By providing measures such as those describing accuracy and significance in useful formats such as confusion matrices, by allowing the user to perform sensitivity analysis on the result, and by presenting the result in alternative ways using for example visualization techniques. 46
    47. 47. Data Mining and Data Warehousing Major challenge to exploit data mining is identifying suitable data to mine. Data mining requires single, separate, clean, integrated, and self-consistent source of data. 47
    48. 48. Data Mining and Data Warehousing A data warehouse is well equipped for providing data for mining. Data quality and consistency is a pre-requisite for mining to ensure the accuracy of the predictive models. Data warehouses are populated with clean, consistent data. 48
    49. 49. Data Mining and Data Warehousing It is advantageous to mine data from multiple sources to discover as many interrelationships as possible. Data warehouses contain data from a number of sources. Selecting the relevant subsets of records and fields for data mining requires the query capabilities of the data warehouse. 49
    50. 50. Data Mining and Data Warehousing The results of a data mining study are useful if there is some way to further investigate the uncovered patterns. Data warehouses provide the capability to go back to the data source. 50