.png){kind=icon}
返回
Data clustering using multivariant optimization algorithm
.png){kind=icon}
PRE
AI
DOI：10.1007/s13042-014-0294-5.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Data clustering is one of the most popular techniques in data mining to group data with great similarity and high dissimilarity into each cluster. This paper presents a new clustering method based on a novel heuristic optimization algorithm proposed recently and named as multivariant optimization algorithm (MOA) to locate the optimal solution automatically through global and local alternating search implemented by a global exploration group and several local exploitation groups. In order to demonstrate the performance of MOA-clustering method, it is applied to group six real-life datasets to obtain their clustering results, which may be compared with those received by employing K-means algorithm, genetic algorithm and particle swarm optimization. The results show that the proposed clustering algorithm is an effective and feasible method to reach a high accurate rate and stability in clustering problems.
Keyword:
Data clustering
Cluster centers
Multivariant optimization algorithm
Global and local optimization
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.7
论文数:
3.2K
被引数:
5.6K
机构
引用论文
.png){kind=icon}
分享收藏Development of top-level requirements for regional aircraft based on the needs of the Russian market
OAAI
OAAI分享收藏分享收藏
查看更多内容