Algorithms for clustering data
Algorithms for clustering data
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
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Efficient Feature Selection in Conceptual Clustering
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Combining Multiple Weak Clusterings
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
On combining multiple clusterings
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Non-redundant clustering with conditional ensembles
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Categorical Data Clustering Using the Combinations of Attribute Values
ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Weighted partition consensus via kernels
Pattern Recognition
On combining multiple clusterings: an overview and a new perspective
Applied Intelligence
Data clustering: a user’s dilemma
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Joint cluster based co-clustering for clustering ensembles
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Determining the number of clusters using information entropy for mixed data
Pattern Recognition
Feature interaction in subspace clustering using the Choquet integral
Pattern Recognition
A theoretic framework of K-means-based consensus clustering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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The category utility function is a partition quality scoring function applied in some clustering programs of machine learning. We reinterpret this function in terms of the data variance explained by a clustering, or, equivalently, in terms of the square-error classical clustering criterion that administers the K-Means and Ward methods. This analysis suggests extensions of the scoring function to situations with differently standardized and mixed scale data.