Scientific discovery: computational explorations of the creative process
Scientific discovery: computational explorations of the creative process
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
The effects of modes of information presentation on decision-making: a review and meta-analysis
Journal of Management Information Systems
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Communications of the ACM
Clustering Algorithms
Visual Explorations in Finance
Visual Explorations in Finance
Visualization Techniques for Mining Large Databases: A Comparison
IEEE Transactions on Knowledge and Data Engineering
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Human Problem Solving
Visual structures for image browsing
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Visualized cognitive knowledge map integration for P2P networks
Decision Support Systems
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In data mining, the usefulness of a data pattern depends on the user of the database and does not solely depend on the statistical strength of the pattern. Based on the premise that heuristic search in combinatorial spaces built on computer and human cognitive theories is useful for effective knowledge discovery, this study investigates how the use of self-organizing maps as a tool of data visualization in data mining plays a significant role in human-computer interactive knowledge discovery. This article presents the conceptual foundations of the integration of data visualization and query processing for knowledge discovery, and proposes a set of query functions for the validation of self-organizing maps in data mining.