... process.REFERENCES[1] Akeel Al-Attar, 1998, DataMining – Beyond Algorithms , http://www.attar.com/tutor /mining. htm.[2] Berry, J. A. Michael; Linoff, Gordon, 1997, DataMining Techniques: For Marketing, Sales, ... of Data Set(training and test set)Filling theempty cellsMUSAFinal AnalysisIs the Data SetComplete?YesNoSelection of completequestionnaires CUSTOMER SATISFACTION USING DATA MINING TECHNIQUES Nikolaos ... Analysis(Consistent family of criteria)Development of questionnaireSurveyMUSA Data Mining Search EnginesRule Induction Engine Data Mining GlobalSatisfaction PredicctionSatisfactionFunctionsPatterns...
... BASED DATAMINING TECHNIQUES The objective of datamining is to extract valuable information from one’s data, to discover the ‘hiddengold’. In Decision Support Management terminology, datamining ... process.REFERENCES[1] Akeel Al-Attar, 1998, DataMining – Beyond Algorithms , http://www.attar.com/tutor /mining. htm.[2] Berry, J. A. Michael; Linoff, Gordon, 1997, DataMining Techniques: For Marketing, Sales, ... one search for patterns of information in data (Parsaye, 1997).Figure 2: Rule Induction process Data miningtechniques are based on data retention and data distillation. Rule induction models...
... of DataMining 33 Table 2.1 DataMining Differs from Typical Operational Business Processes TYPICAL OPERATIONAL SYSTEM DATAMINING SYSTEM Operations and reports on Analysis on historical data ... of techniques to apply in a particular situation depends on the nature of the datamining task, the nature of the available data, and the skills and preferences of the data miner. Data mining ... that, on a technical level, the datamining effort is working and the data is reasonably accurate. This can be quite comforting. If the data and the dataminingtechniques applied to it are powerful...
... Applications and Trends in DataMining 64911.1 DataMining Applications 64911.1.1 DataMining for Financial Data Analysis 64911.1.2 DataMining for the Retail Industry 65111.1.3 DataMining for the Telecommunication ... Commercial DataMining Systems 66311.3 Additional Themes on DataMining 66511.3.1 Theoretical Foundations of DataMining 66511.3.2 Statistical DataMining 66611.3.3 Visual and Audio DataMining ... Motivated Data Mining? Why Is It Important? 11.2 So, What Is Data Mining? 51.3 DataMining On What Kind of Data? 91.3.1 Relational Databases 101.3.2 Data Warehouses 121.3.3 Transactional Databases...
... level data, 96 publications Building the Data Warehouse (Bill Inmon), 474 Business Modeling and DataMining (Dorian Pyle), 60 Data Preparation for DataMining (Dorian Pyle), 75 The Data ... Business Modeling and Data Mining, 60 Data Preparation for Data Mining, 75 470643 bindex.qxd 3/8/04 11:08 AM Page 619C Index 619 calculations, probabilities, 133–135 call detail databases, 37 call-center ... discussed, 7 Data Preparation for DataMining (Dorian Pyle), 75 The Data Warehouse Toolkit (Ralph Kimball), 474 data warehousing customer patterns, 5 for decision support, 13 discussed, 4 database...
... factor in turn. Data Is Being Produced Data mining makes the most sense when there are large volumes of data. In fact, most dataminingalgorithms require large amounts of data in order to ... of DataMining 33 Table 2.1 DataMining Differs from Typical Operational Business Processes TYPICAL OPERATIONAL SYSTEM DATAMINING SYSTEM Operations and reports on Analysis on historical data ... Although algorithms are important, the datamining solu-tion is more than just a set of powerful techniques and data structures. The techniques have to be applied in the right areas, on the right data. ...
... c04.qxd 3/8/04 11:10 AM Page 97 Data Mining Applications 97 mining techniques used to generate the scores. It is worth noting, however, that many of the dataminingtechniques in this book can ... relationships suggest new hypotheses to test and the datamining process begins all over again. Lessons Learned Data mining comes in two forms. Directed datamining involves searching through historical ... independent of the data 470643 c04.qxd 3/8/04 11:10 AM Page 87 Data Mining Applications in Marketing and Customer Relationship Management 4 CHAPTER Some people find dataminingtechniques interesting...
... of Statistics: DataMining Using Familiar Tools 127 Looking at Discrete Values Much of the data used in datamining is discrete by nature, rather than contin-uous. Discrete data shows up in ... can be useful for several datamining techniques, such as clustering and neural net-works. Other uses of the z-value are covered in Chapter 17, which discusses data transformations. -2 -1 ... known. Most of the directed dataminingtechniques discussed in this book can be used to build a classification model to assign people to segments based on available data. All that is needed is...
... in several areas: ■■ Data miners tend to ignore measurement error in raw data. ■■ Data miners assume that there is more than enough data and process-ing power. ■■ Datamining assumes dependency ... 11:11 AM Page 159The Lure of Statistics: DataMining Using Familiar Tools 159 statisticians use similar techniques to solve similar problems, the datamining approach differs from the standard ... represent spurious patterns that might be picked up by datamining algorithms. One major difference between business data and scientific data is that the latter has many continuous values and...
... their ability to generalize and learn from data mimics, in some sense, our own ability to learn from experience. This ability is useful for data mining, and it also makes neural networks an ... net-works as predictive modeling tools. At the end, we see how they can be used for undirected datamining as well. A good place to begin is, as always, at the beginning, with a bit of history. ... CHAPTER Artificial neural networks are popular because they have a proven track record in many datamining and decision-support applications. Neural networks— the “artificial” is usually dropped—are...
... calls, which can then be applied to data. These patterns can be turned into new features of the data, for use in conjunction with other directed datamining techniques. 470643 c11.qxd 3/8/04 ... automatic cluster detection algorithms themselves are simply finding structure that exists in the data without regard to any particular target variable. Most data mining tasks start out with ... zones for a major daily newspaper. Searching for Islands of Simplicity In Chapter 1, where dataminingtechniques are classified as directed or undi-rected, automatic cluster detection is described...
... on extracting every last bit of information out of a few hundred data points. In data mining applications, the volumes of data are so large that statistical con-cerns about confidence and ... measurement of customer retention. Any reasonable database that purports to be about customers should have this data readily accessible. Of course, marketing databases are rarely simple. There are two ... the data speak instead of finding a special function to speak for it. Empirical hazard probabilities simply let the historical data determine what is likely to happen, without trying to fit data...
... redundant. Choosing a DataMining Technique The choice of which datamining technique or techniques to apply depends on the particular datamining task to be accomplished and on the data available ... cho-sen. However, some kinds of data pose particular problems for some data min-ing techniques. Data Type Categorical variables are especially problematic for dataminingtechniques that use the ... understood the data, people who understood the datamining techniques, peo-ple who understood the business problem to be addressed, and at least one person with experience applying datamining to...