... of the datamining task, the nature of the available data, and the skills and preferences of the data miner. Data mining comes in two flavors—directed and undirected. Directed 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 dataand the dataminingtechniques applied to it are powerful ... the datamining effort itself. If we cannot measure the results of mining the data, then we can-not learn from the effort and there is no virtuous cycle. Measurements of past efforts and ad...
... performance and wide area datamining systems for over ten years. More recently, he has worked on standards and testbeds fordata mining. He has an AB in Mathematics from Harvard University and ... the datamining group in the centre. He has been working on distributed datamining algorithms and systems development. He is also working on network infrastructure for global wide datamining ... J., To, H.W., and Yang, D. Large scale data mining: Challenges and responses. Proc. of the Third Int’l Conference on Knowledge Discovery andData Mining. Goil, S., Alum, S., and Ranka, S....
... level data, 96 publications Building the Data Warehouse (Bill Inmon), 474 Business Modeling andDataMining (Dorian Pyle), 60 Data Preparation forDataMining (Dorian Pyle), 75 The Data ... 89–90 metadata repository, 484, 491 methodologies data correction, 72–74 data exploration, 64–68 data mining process, 54–55 data selection, 60–64 data transformation, 74–76 data translation, ... Business Modeling andData Mining, 60 Data Preparation forData Mining, 75 470643 bindex.qxd 3/8/04 11:08 AM Page 619C Index 619 calculations, probabilities, 133–135 call detail databases, 37...
... of the datamining task, the nature of the available data, and the skills and preferences of the data miner. Data mining comes in two flavors—directed and undirected. Directed data mining ... hours for reports System of record fordata Copy of data Descriptive and repetitive Creative First, problems being addressed by datamining differ from operational problems—a datamining ... the datamining effort itself. If we cannot measure the results of mining the data, then we can-not learn from the effort and there is no virtuous cycle. Measurements of past efforts and ad...
... before. The newly discovered relationships suggest new hypotheses to test and the datamining process begins all over again. Lessons Learned Data mining comes in two forms. Directed datamining ... 11:10 AM Page 97 Data MiningApplications 97 mining techniques used to generate the scores. It is worth noting, however, that many of the dataminingtechniques in this book can and have been ... California based on data that excludes calls to Los Angeles. Step Six: Transform Data to Bring Information to the Surface Once the data has been assembled and major data problems fixed, the data...
... standard deviation (strictly speaking, this is the standard error but the two are equivalent for our purposes) and the mean value and the sample size for a proportion. This is called the standard ... reasons as well. For instance, it is one way of taking several variables and converting them to similar ranges. This can be useful for several datamining techniques, such as clustering and neural ... 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 the form...
... customers in California for the challenger and everyone else for the champion. ■■ Use the 5 percent lowest and 5 percent highest value customers for the challenger, and everyone else for the champion. ... in several areas: ■■ Data miners tend to ignore measurement error in raw data. ■■ Data miners assume that there is more than enough dataand process-ing power. ■■ Datamining assumes dependency ... that might be picked up by datamining algorithms. One major difference between business dataand scientific data is that the latter has many continuous values and the former has many discrete...
... common for neural networks are the logistic and the hyperbolic tangent. The major difference between them is the range of their outputs, between 0 and 1 for the logistic and between –1 and 1 for ... generalize and learn from data mimics, in some sense, our own ability to learn from experience. This ability is useful fordata mining, and it also makes neural networks an exciting area for research, ... test set to see how well it performs. 7. Apply the model generated by the network to predict outcomes for unknown inputs. Fortunately, datamining software now performs most of these steps auto-matically....
... detection is used to evaluate editorial zones for a major daily newspaper. Searching for Islands of Simplicity In Chapter 1, where dataminingtechniques are classified as directed or undi-rected, ... inexplicable and perhaps unimportant. In a broader sense, however, clustering can be a directed activity because clusters are sought for some business purpose. In marketing, clusters formed for a ... 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 11:17 AM Page 355Automatic Cluster...
... and Lo [24] examine network performance for supporting Internet telephony.They use UDP to collect data between several sites, and then apply a static playout buffer and losscompensation to determine ... takeoccasional “snapshots” of Internet performance, obtaining new data between new sites. Our newtraces add to the overall amount of data available on Internet performance. Secondly, most ofthe ... existing protocols for resource discovery (and finding themlacking for wide area applications) , we present a scalable protocol for wide area service discovery,which is ideal for discovery of gateways,...
... 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, and Customer ... 1998, ‘A datamining application for issuing predictions, summarizing the data and revealing interesting phenomena’, http://www.wizsoft.com/why.html.[7] Mihelis G.; Grigoroudis E.; and Siskos ... of Data Set(training and test set)Filling theempty cellsMUSAFinal AnalysisIs the Data SetComplete?YesNoSelection of completequestionnaires CUSTOMER SATISFACTION USING DATA MINING TECHNIQUES Nikolaos...
... 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 in which one search for patterns of information in data (Parsaye, 1997).Figure 2: Rule Induction process Data miningtechniques are based on data retention anddata distillation. Rule induction ... 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, and Customer...