Rough k-means clustering using the information of cluster center displacement
碩士 === 國立臺灣海洋大學 === 資訊工程學系 === 96 === Clustering algorithm is a useful method to analyze data. In this paper, we present a fast rough k-means clustering algorithm to solve the unstable problem encountered in available approaches. Our method is more efficient and stable than existing methods. To solv...
Main Authors: | Shih-Huang Tsai, 蔡世煌 |
---|---|
Other Authors: | Zone-Chang Lai |
Format: | Others |
Language: | en_US |
Published: |
2008
|
Online Access: | http://ndltd.ncl.edu.tw/handle/13774962132005969400 |
Similar Items
-
Divisive K-Means Clustering Algorithm for Determining k and Positions of Cluster Centers
by: Lin, You-Shin, et al.
Published: (2009) -
Generalized Fuzzy k-Means Clustering Using m Nearest Cluster Centers
by: 賴仁傑
Published: (2013) -
Speeding up Generalized Fuzzy k-Means Clustering Using m Nearest Cluster Centers Algorithm on GPU
by: Dinh-Trung Vu, et al.
Published: (2015) -
A k-means algorithm to optimize the initial cluster centers
by: ZHANG Mingwei, et al.
Published: (2016-10-01) -
Integrated Rough Set based on K-means and CPDA to Extract the Rules of Library Borrowing Clusters
by: Red-Lang Wang, et al.
Published: (2008)