Data Mining: IV(7 Management Information Systems)

In this volume researchers, application developers and practitioners from many different areas share state-of-the-art research results and practical development experiences. All of the contributions featured were originally presented at the Fourth International Conference on Data Mining, the broad objective of which was to discuss the development of innovative algorithms and data structures to speed up computations. Reflecting the latest trends in this rapidly evolving area, the contributions focus in particular on building applications for customer relationship management (CRM) and competitive intelligence.

Table of Contents:
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)