This paper presents a general framework for agglomerative hierarchical clustering based on graphs. Different hierarchical agglomerative clustering algorithms can be obtained from this framework, by specifying an inter...
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(纸本)0769525210
This paper presents a general framework for agglomerative hierarchical clustering based on graphs. Different hierarchical agglomerative clustering algorithms can be obtained from this framework, by specifying an inter-cluster similarity measure, a subgraph of the beta-similarity graph, and a cover routine. We also describe two methods obtained from this framework called Hierarchical Compact Algorithm and Hierarchical Star Algorithm. These algorithms have been evaluated using standard document collections. The experimental results show that our methods are faster and obtain smaller hierarchies than traditional hierarchical algorithms while achieving a similar clustering quality.
This paper presents a general framework for agglomerative hierarchical clustering based on graphs. Different hierarchical agglomerative clustering algorithms can be obtained from this framework, by specifying an inter...
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This paper presents a general framework for agglomerative hierarchical clustering based on graphs. Different hierarchical agglomerative clustering algorithms can be obtained from this framework, by specifying an inter-cluster similarity measure, a subgraph of the 13-similarity graph, and a cover routine. We also describe two methods obtained from this framework called hierarchical compact algorithm and hierarchical star algorithm. These algorithms have been evaluated using standard document collections. The experimental results show that our methods are faster and obtain smaller hierarchies than traditional hierarchical algorithms while achieving a similar clustering quality
In this paper we introduce a general framework for hierarchical clustering that deals with both static and dynamic data sets. From this framework, different hierarchical agglomerative algorithms can be obtained, by sp...
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In this paper, we present an incremental clustering algorithm in the logical combinatorial approach to patternrecognition, which finds incrementally the β0-compact sets with radius a of an object collection. The pro...
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