- Title
- K-AP clustering algorithm for large scale dataset
- Creator
- Liu, Chao; Hay, Rosemary; Wang, Wei
- Date
- 2011
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/41703
- Identifier
- vital:4557
- Identifier
-
https://doi.org/10.1109/IWCDM.2011.28
- Abstract
- Affinity propagation clustering algorithm is with a broad value in science and engineering because of it no need to input the number of clusters in advances, robustness and good generalization. But the algorithm needs the initial similarity (the distance between any two points) as a parameter, a lot of time and storage space is required for the calculation of similarity. It's limited to apply to cluster of the large amounts of data. To solve problem, this paper brings forward K-AP cluster algorithm which integrate k-means algorithm to AP algorithm to decrease time-consuming and space superiority. The results show the K-AP algorithm is faster than the original algorithm processing in speed, and it can cluster large amounts of data, and achieve better results. © 2011 IEEE.
- Publisher
- Nanjing, Jiangsu
- Rights
- This metadata is freely available under a CCO license
- Subject
- AP algorithm; K-means; Space complexity; Time complexity; Affinity propagation; Cluster algorithms; Data sets; K-Means algorithm; Large amounts of data; Number of clusters; Original algorithms; Science and engineering; Space superiority; Storage spaces; Two-point; Data mining
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