Data Mining: 2nd

Section 1: Applications of Data Mining in Science, Engineering, Business, Industry and Medicine - Web mining through the online analyst; Bayesian networks for knowledge discovery in a database from the program for genetic improvement of the Nelore breed; Using a data mining workbench for micro and macro economic modelling; Small business modeling within the financial accounting conceptual framework; Paperwork reduction by means of data mining; CRM in a real-world insurance company; A data-mining alternative to model hospital operations: clinical costs and predictions; Optimal entrocopy encoders for mining multiply resolved data; Acquisition of KANSEI based on fuzzy inference and multivaritate analysis; Data mining for database marketing at Garanti Bank; Use of equation discovery: Oscillating flow in a U-tube; The use of learning classifier systems in the direct marketing industry. Section 2: Data Warehousing and Databases - KR and model discovery from active DB with predictive logic; Metadata - based data auditing; An analysis of the integration between data mining applications and database systems; Design of a data warehouse to support the management of academic institutions. Section 3: Internet Applications - Automatic construction of ontology from text databases; Attractability: an indicator for optimising the design of a web site. Section 4: Data Mining Methodologies - A new algorithm for finding association rules; Local feature selection for heterogeneous problems; Content in context: a data-driven approach. Section 5: Knowledge Discovery and Data Mining - An architecture for ABEN-KDD- an agent-based environment for knowledge discovery in databases; Discovering graph structures in high dimensional spaces; Discovering salient data features by composing and manipulating logical equations; A qualitative spatial reasoning approach in knowledge discovery in spatial databases; A fuzzy - based conceptual KDD approach: the SaintEtiQ system; Supervised knowledge discovery from incomplete data; Data scale reduction via instances summarization using the Rough Set Theory; Data mining in temporal sequences: a technique based on MC; Data mining telecommunications network data for fault management and development testing; Higher order mining: modelling and mining the results of knowledge discovery; A computational environment for extracting rules from databases; Considerations about the effectiveness of inductive learning process in data-mining context. Section 6: Neural Networks and Decision Trees - The deterministic evolutionary learning algorithm; Wrapped feature selection for binary classification Bayesian regularisation neural networks: a database marketing application; Web text mining using a hybrid intelligent system based on KDT, expert system and neural network; Alleviating the complexity of the Combinatorial Neural Model using a committee machine; Application of decision tree classifiers to computer intrusion detection; Effects of attribute selection measures and sampling policies on functional structures of decision trees. Section 7: Genetic Algorithms and Parallel Techniques - Credit approval by a clustering genetic algorithm; Designing optimized pattern recognition systems by learning Voronoi vectors using genetic algorithms; Scalable parallel algorithms for predictive modelling. Section 8: Visualisation in Data Mining - Interactive rule-network layout with a genetic algorithm in a knowledge discovery process; Visualisation for Data Mining telecommunications network data; InfoZoom - Analysing Formula One racing results with an interactive data mining and visualisation tool. Section 9: Clustering and Classification Techniques - Evolving TSK fuzzy rules for classification tasks by Genetic Algorithms; Hierachical clustering for data mining by RBF network; Detecting visual feature importance via tree classifiers. An experience; Input dependent misclassification costs for cost-sensitive classifiers; Undirect knowledge discovery by using singular value decomposition; A clustering algorithm using the tabu search approach with simulated annealing; Cluster generation using tabu search based maximum descent algorithm; Stabilization of regression trees; Influence of lossy compression on hyperspectral image classification accuracy. Section 10: Tools for Pattern Discovery - Modeling dynamical systems by recurrent neural networks; A visual data mining tool to support cooperative learning; A new insight into the algebraic structure of the exponential smoothing algorithm of Brown. Section 11: Case Studies - A case based reasoning framework to extract knowledge from data; Mining customer preference ratings for product recommendation using the support vector machine and the latent class model; Data mining a large health insurance database; Case study of a retail bank marketing datamart development.