Unraveling the semantics of conceptual schemas
Communications of the ACM
Organization and retrieval in a pictorial digital library
DL '97 Proceedings of the second ACM international conference on Digital libraries
An error-based conceptual clustering method for providing approximate query answers
Communications of the ACM - Electronic supplement to the December issue
Generality-Based Conceptual Clustering with Probabilistic Concepts
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Applied Intelligence
A Conceptual Clustering Algorithm for Database Schema Design
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Unsupervised Learning with Mixed Numeric and Nominal Data
IEEE Transactions on Knowledge and Data Engineering
SAINTETIQ: a fuzzy set-based approach to database summarization
Fuzzy Sets and Systems - Data bases and approximate reasoning
LC: A Conceptual Clustering Algorithm
MLDM '01 Proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition
Data mining tasks and methods: Clustering: conceptual clustering
Handbook of data mining and knowledge discovery
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Computers in Human Behavior
Creating hierarchical categories using cell assemblies
Connection Science
Adaptive web sites: conceptual cluster mining
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Concept formation vs. logistic regression: predicting death in trauma patients
Artificial Intelligence in Medicine
Automated design of diagnostic systems
Artificial Intelligence in Medicine
Paper: Learning and discovery from a clinical database: An incremental concept formation approach
Artificial Intelligence in Medicine
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In this paper we describe WITT, a computational model of categorization and conceptual clustering that has been motivated and guided by research on human categorization. Properties of categories to which humans are sensitive include best or prototypical members, relative contrasts between categories, and polymorphy (neither necessary nor sufficient feature rules). The system uses pairwise feature correlations to determine the “similarity” between objects and clusters of objects, allowing the system a flexible representation scheme that can model common-feature categories and polymorphous categories. This intercorrelation measure is cast in terms of an information-theoretic evaluation function that directs WITT'S search through the space of clusterings. This information-theoretic similarity metric also can be used to explain basic-level and typicality effects that occur in humans. WITT has been tested on both artificial domains and on data from the 1985 World Almanac, and we have examined the effect of various system parameters on the quality of the model's behavior.