A Parallel Minimal-Spanning-Tree-Based Clustering Algorithm with Self-Detectability of the Best Number of Clusters
碩士 === 國立中央大學 === 資訊工程研究所 === 100 === Recently, efficiency of clustering algorithms for large data analysis has become a major issue in many application domains. First, the computation time and storage space increases dramatically while processing a large amount of data. Second, it is hard to determ...
Main Authors: | Shi-Shan Chen, 陳詩姍 |
---|---|
Other Authors: | Wei-Jen Wang |
Format: | Others |
Language: | zh-TW |
Published: |
2012
|
Online Access: | http://ndltd.ncl.edu.tw/handle/36517890026743833619 |
Similar Items
-
Fast Parallel Algorithms for Euclidean Minimum Spanning Tree and Hierarchical Spatial Clustering
by: Wang, Yiqiu, et al.
Published: (2022) -
Density Peaks Clustering Based on Local Minimal Spanning Tree
by: Renmin Wang, et al.
Published: (2019-01-01) -
On the clustered spanning tree problem
by: Lin, Chen-Wan, et al.
Published: (2017) -
CciMST: A Clustering Algorithm Based on Minimum Spanning Tree and Cluster Centers
by: Xiaobo Lv, et al.
Published: (2018-01-01) -
Double-Valued Neutrosophic Sets, their Minimum Spanning Trees, and Clustering Algorithm
by: Kandasamy Ilanthenral
Published: (2018-03-01)