A Geometry-Based Accelerated Fusion Clustering Algorithm and its Application in Marine Engineering
| Autor: | Tian Zhen Wang, Tian Hao Tang, Yang Liu |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
DBSCAN
Clustering high-dimensional data k-medoids Computer science Correlation clustering General Engineering k-means clustering Geometry computer.software_genre Determining the number of clusters in a data set Biclustering Data stream clustering SUBCLU CURE data clustering algorithm Consensus clustering Canopy clustering algorithm Affinity propagation Data mining Cluster analysis computer FSA-Red Algorithm Marine engineering |
| Zdroj: | Advanced Materials Research. :106-111 |
| ISSN: | 1662-8985 |
| DOI: | 10.4028/www.scientific.net/amr.108-111.106 |
| Popis: | In order to solve the problem in k-means algorithm that inappropriate selection of initial clustering centers often causes clustering in local optimum and the time complexity is too high when handling large amounts of data, a fusion clustering algorithm based on geometry is proposed in this paper. The result of experiments shows this algorithm is better than the traditional k-means and the k-means++ algorithms, with higher quality and faster speed. And at last in this paper, we apply it in marine engineering. |
| Databáze: | OpenAIRE |
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