Data Mining: IV

Section 1 Data and text mining: Scanning once a large distributed database to mine global association rules by growing a prefix tree for each local transaction; Network mining for managing a broadband network; A comparison of some tree based prediction tools; An ontology engineering approach for knowledge discovery from data in evolving domains; Pattern distance of time series; Learning patterns through artificial contrasts with application to process control; Preprocessing method and similarity measures in clustering-based text mining: a preliminary study; Towards on an optimized parallel KNN-FUZZY classification approach; Global model of distributed data mining is not summing of local models; Management of R&D projects using the data mining strategy; Expertise location: can text mining help?; Critical analysis reporting environment (CARE); An instrument to support data mining projects; Mining text databases on clients opinion for oil industry; Identification of blade vibration causes in wind turbine generators; Association rule mining using list representation. Section 2 Clustering: Introducing prior knowledge into the clustering process; A feature selection Bayesian approach for a clustering genetic algorithm; Genetic algorithm using iterative shrinking for solving clustering problems; Starcluster: a visualization, clustering and classification tool; Identifying the insolvency profile in a telephony operator database; CloNI: clustering of -interval discretization; Modelling retail bank customers' needs with Bayesian Belief Networks. Section 3 Categorization: Data classification by a fuzzy genetic system approach; One-against-all multicategory classification via discrete support vector machines; Alternative strategies for decision list construction; Getting a clearer picture: a business application of data mining; Convex hulls as an hypothesis language bias. Section 4 CRM: A screening tool based on neural networks; Ontologies, CRM, Data Mining: How to integrate?; Quantifying credit-scoring performance; Investigating purchasing patterns for financial services using Markov and MTD models; The impact of product features and intermediaries on customer retention; Using data mining to estimate customer's buying potential; Manufacturer-retailer promotion competition: customisation of coupon target selection; The application of CRM in call centers; Profiling in an augmented-commerce environment. (part contents)