Principles of Data Mining and Knowledge Discovery

Knowledge discovery — A control theory perspective.- Modelling customer retention with Rough Data Models.- Share based measures for itemsets.- Parallel knowledge discovery using domain generalization graphs.- Rough set theory and rule induction techniques for discovery of attribute dependencies in medical information systems.- Logical calculi for knowledge discovery in databases.- Extraction of experts' decision process from clinical databases using rough set model.- Discovering of health risks and case-based forecasting of epidemics in a health surveillance system.- An algorithm for multi-relational discovery of subgroups.- Finding similar time series.- Exploration of document collections with self-organizing maps: A novel approach to similarity representation.- Pattern based browsing in document collections.- Induction of fuzzy characteristic rules.- Regression-based classification methods and their comparison with decision tree algorithms.- Attribute discovery and rough sets.- Generation of rules from incomplete information systems.- Knowledge discovery from software engineering data: Rough set analysis and its interaction with goal-oriented measurement.- Efficient multisplitting on numerical data.- SNOUT: An intelligent assistant for exploratory data analysis.- Exploratory analysis of biochemical processes using hybrid modeling methods.- Using signature files for querying time-series data.- A new and versatile method for association generation.- Bivariate decision trees.- Towards process-oriented tool support for knowledge discovery in databases.- A connectionist approach to structural similarity determination as a basis of clustering, classification and feature detection.- Searching for relational patterns in data.- Finding spatial clusters.- Interactive interpretation of hierarchical clustering.- The principle of transformation between efficiency and effectiveness: Towards a fair evaluation of the cost-effectiveness of KDD techniques.- Recognizing reliability of discovered knowledge.- Clustering techniques in biological sequence analysis.- TOAS intelligence mining; analysis of natural language processing and computational linguistics.- Algorithms for constructing of decision trees.- Mining in the phrasal frontier.- Mining time series using rough sets — A case study.- Neural networks design: Rough set approach to continuous data.- On meta levels of an organized society of KDD agents.- Using neural network to extract knowledge from database.- Induction of strong feature subsets.- Rough sets for data mining and knowledge discovery.- Techniques and applications of KDD.- A tutorial introduction to high performance data mining.- Data mining in the telecommunications industry.