1. A Novel Locality Sensitive K-Means Clustering Algorithm based on Core Clusters
- Author
-
Lei Gu
- Subjects
Clustering high-dimensional data ,Fuzzy clustering ,Brown clustering ,business.industry ,Computer science ,Single-linkage clustering ,Correlation clustering ,k-means clustering ,Pattern recognition ,General Medicine ,computer.software_genre ,Graph ,Determining the number of clusters in a data set ,Data stream clustering ,CURE data clustering algorithm ,Consensus clustering ,Canopy clustering algorithm ,Affinity propagation ,FLAME clustering ,Artificial intelligence ,Data mining ,business ,Cluster analysis ,computer ,k-medians clustering - Abstract
The locality sensitive k-means clustering method has been presented recently. Although this approach can improve the clustering accuracies, it often gains the unstable clustering results because some random samples are employed for the initial centers. In this paper, an initialization method based on the core clusters is used for the locality sensitive k-means clustering. The core clusters can be formed by constructing the σ-neighborhood graph and their centers are regarded as the initial centers of the locality sensitive k-means clustering. To investigate the effectiveness of our approach, several experiments are done on three datasets. Experimental results show that our proposed method can improve the clustering performance compared to the previous locality sensitive k-means clustering.
- Published
- 2013