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 KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Communications of the ACM
Mining massively incomplete data sets by conceptual reconstruction
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Visual Explorations in Finance
Visual Explorations in Finance
On the autocorrelation structure of TCP traffic
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Advances in modeling and engineering of Longe-Range dependent traffic
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
Visualizing queries on databases of temporal histories: new metaphors and their evaluation
Data & Knowledge Engineering - Special issue: Temporal representation and reasoning
Mining Optimized Gain Rules for Numeric Attributes
IEEE Transactions on Knowledge and Data Engineering
Privacy-aware collection of aggregate spatial data
Data & Knowledge Engineering
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The raw survey data for data mining are often incomplete. The issues of missing data in knowledge discovery are often ignored in data mining. This article presents the conceptual foundations of data mining with incomplete survey data, and proposes query processing for knowledge discovery and a set of query functions for the conceptual construction in survey data mining. Through a case, this paper demonstrates that conceptual construction on incomplete data can be accomplished by using artificial intelligence tools such as self-organizing maps.