Algorithms for clustering data
Algorithms for clustering data
Computational models of concept learning
Concept formation knowledge and experience in unsupervised learning
The formation and use of abstract concepts in design
Concept formation knowledge and experience in unsupervised learning
Applying AI Clustering to Engineering Tasks
IEEE Expert: Intelligent Systems and Their Applications
Conceptual Clustering, Categorization, and Polymorphy
Machine Learning
Experiments with Incremental Concept Formation: UNIMEM
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Iterative optimization and simplification of hierarchical clusterings
Journal of Artificial Intelligence Research
Unifying metric approach to the triple parity
Artificial Intelligence
Temporal Probabilistic Concepts from Heterogeneous Data Sequences
Soft-Ware 2002 Proceedings of the First International Conference on Computing in an Imperfect World
Comparison of Three Objective Functions for Conceptual Clustering
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Mining intrusion detection alarms for actionable knowledge
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge discovery by probabilistic clustering of distributed databases
Data & Knowledge Engineering
MPM: a hierarchical clustering algorithm using matrix partitioning method for non-numeric data
Journal of Intelligent Information Systems
A clustering algorithm based on maximal θ-distant subtrees
Pattern Recognition
Hierarchical Distance-Based Conceptual Clustering
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
An Instantiation of Hierarchical Distance-Based Conceptual Clustering for Propositional Learning
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Learning fuzzy concept hierarchy and measurement with node labeling
Information Systems Frontiers
Learning fuzzy concept hierarchy and measurement with node labeling
ISPA'07 Proceedings of the 2007 international conference on Frontiers of High Performance Computing and Networking
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Statistical research in clustering has almost universally focused on data sets described by continuous features and its methods are difficult to apply to tasks involving symbolic features. In addition, these methods are seldom concerned with helping the user in interpreting the results obtained. Machine learning researchers have developed conceptual clustering methods aimed at solving these problems. Following a long term tradition in AI, early conceptual clustering implementations employed logic as the mechanism of concept representation. However, logical representations have been criticized for constraining the resulting cluster structures to be described by necessary and sufficient conditions. An alternative are probabilistic concepts which associate a probability or weight with each property of the concept definition. In this paper, we propose a symbolic hierarchical clustering model that makes use of probabilistic representations and extends the traditional ideas of specificity-generality typically found in machine learning. We propose a parameterized measure that allows users to specify both the number of levels and the degree of generality of each level. By providing some feedback to the user about the balance of the generality of the concepts created at each level and given the intuitive behavior of the user parameter, the system improves user interaction in the clustering process.