Sublinear time approximate clustering
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Mining intrusion detection alarms for actionable knowledge
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A k-Median Algorithm with Running Time Independent of Data Size
Machine Learning
A New Conceptual Clustering Framework
Machine Learning
A data mining approach for analysis of worm activity through automatic signature generation
Proceedings of the 1st ACM workshop on Workshop on AISec
A hybrid heuristic approach for attribute-oriented mining
Decision Support Systems
Hi-index | 0.00 |
Research in cluster analysis has resulted in a large number of algorithms and similarity measurements for clustering scientific data. Machine learning researchers have published a number of methods for conceptual clustering, in which observations are grouped into clusters that have “good” descriptions in some language. In this paper we investigate the general properties that similarity metrics, objective functions, and concept description languages must have to guarantee that a (conceptual) clustering problem is polynomial-time solvable by a simple and widely used clustering technique, the agglomerative-hierarchical algorithm. We show that under fairly general conditions, the agglomerative-hierarchical method may be used to find an optimal solution in polynomial time.