Improved K-means Algorithm Based on Optimizing Initial Cluster Centers and Its Application
作者: Xue LinyaoWang Jianguo
论文集英文名称: Proceedings of the 2018 Second International Conference of Sensor Network and Computer Engineering (ICSNCE 2018)
会议英文名称: >Proceedings of the 2018 Second International Conference of Sensor Network and Computer Engineering (ICSNCE 2018), 2018
来源数据库: Atlantis Press
DOI: 10.2991/icsnce-18.2018.2
原始语种摘要: Data mining is a process of data grouping or partitioning from the large and complex data, and the clustering analysis is an important research field in data mining. The K-means algorithm is considered to be the most important unsupervised machine learning method in clustering, which can divide all the data into k subclasses that are very different from each other. By constantly iterating, the distance between each data object and the center of its subclass is minimized. Because K-means algorithm is simple and efficient, it is applied to data mining, knowledge discovery and other fields. However, the algorithm has its inherent shortcomings, such as the K value in the K-means algorithm needs to be given in advance; clustering results are highly dependent on the selection of initial...
全文获取路径: Atlantis出版社 
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关键词翻译
关键词翻译
  • clustering 聚类
  • means 手段
  • centers 机组间距
  • mining 矿业
  • partitioning 分块
  • optimize 优选
  • algorithm 算法
  • efficient 有用的
  • discovery 发现
  • Application 应用