...
首页> 外文期刊>International Journal of Hybrid Intelligent Systems >Empirical evaluation of five algorithms for the initialization phase of the k-Means algorithm
【24h】

Empirical evaluation of five algorithms for the initialization phase of the k-Means algorithm

机译:k均值算法初始化阶段的五种算法的经验评价

获取原文
获取原文并翻译 | 示例
           

摘要

A recurring problem in a wide variety of research areas such as pattern recognition, machine learning, data mining and statistics, among others, is characterized as a clustering problem. Such a problem can be described in a simplistic way as: given a set of data (observations, objects, points, etc.), group similar data into clusters (groups). A clustering of a given data set is then characterized as a set of clusters, in which elements belonging to a cluster are similar to each other and elements belonging to distinct clusters are not similar. Clustering algorithms are non-supervised algorithms and, among the many available in the literature, the k-Means, that uses a random initizalization process, can be considered one of the most popular and successful. The performance of the k-Means, however, is highly dependent on a ‘good’ initialization of the k cluster centers (centroids), as well as on the value assigned to the number (k ) of clusters the final clustering should have. This paper addresses experiments using five initialization algorithms available in the literature namely, the Method1, the k-Means++, the CCIA, the Maedeh and Suresh and the SPSS algorithms, to empirically evaluate their contribution for improving the k-Means performance.
机译:在各种研究领域的重复问题,如模式识别,机器学习,数据挖掘和统计数据,等特征在于聚类问题。可以以简单化方式描述这样的问题:给定一组数据(观察,对象,点等),将类似的数据分组到群集中(组)。然后,给定数据集的群集被表征为一组簇,其中属于群集的元素彼此相似,并且属于不同群集的元素是不相似的。聚类算法是非监督算法,并且在文献中的许多可用中,使用随机发电过程的K-means,可以被视为最受欢迎和成功的一个。然而,k均值的性能高度依赖于“良好”初始化K集群中心(质心),以及最终聚类应该具有的群集的数量(k)。本文通过文献中可用的五种初始化算法来解决实验,即方法1,K-Means ++,CCIA,Maedeh和Suresh和SPSS算法,以凭经验评估它们对提高K-Means性能的贡献。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号