A data selection framework for k-means algorithm to mine high precision clusters

Zhengzheng Lou, Chaoyang Zhang
{"title":"A data selection framework for k-means algorithm to mine high precision clusters","authors":"Zhengzheng Lou, Chaoyang Zhang","doi":"10.1109/FSKD.2017.8393013","DOIUrl":null,"url":null,"abstract":"Traditional clustering algorithms employ all the data items to learn the cluster patterns. However, in real-world applications, some data show clear coherent behaviour and can be summarized well, while some data present weak tendencies to be assigned to any particular pattern. For such situation, this paper presents a data selection framework for K-Means algorithm to get high precision clusters from the data collection. It differs from traditional k-means-type algorithms in three respects. First, in the cluster learning process, we take the changed value of cluster's Bregman Information, which is generated by merging one data item into the potential clusters, as the measure of data item's clustering tendency. Second, only data items with strong clustering tendencies, that is the changed value of cluster's Bregman Information is less than the predefined radius, are selected to learn the cluster patterns, while the remaining data points are ignored and belong to no cluster. The clustering is non-exhaustive. Third, the radius of the clusters can be changed in the learning process. It is a dynamic learning framework. Experiments on synthetic, document and image data show the effectiveness of the proposed algorithm.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"334 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FSKD.2017.8393013","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

Traditional clustering algorithms employ all the data items to learn the cluster patterns. However, in real-world applications, some data show clear coherent behaviour and can be summarized well, while some data present weak tendencies to be assigned to any particular pattern. For such situation, this paper presents a data selection framework for K-Means algorithm to get high precision clusters from the data collection. It differs from traditional k-means-type algorithms in three respects. First, in the cluster learning process, we take the changed value of cluster's Bregman Information, which is generated by merging one data item into the potential clusters, as the measure of data item's clustering tendency. Second, only data items with strong clustering tendencies, that is the changed value of cluster's Bregman Information is less than the predefined radius, are selected to learn the cluster patterns, while the remaining data points are ignored and belong to no cluster. The clustering is non-exhaustive. Third, the radius of the clusters can be changed in the learning process. It is a dynamic learning framework. Experiments on synthetic, document and image data show the effectiveness of the proposed algorithm.
基于k均值算法的高精度聚类数据选择框架
传统的聚类算法利用所有的数据项来学习聚类模式。然而,在实际应用中,一些数据表现出清晰的连贯行为,可以很好地总结,而一些数据则表现出分配给任何特定模式的弱趋势。针对这种情况,本文提出了一种K-Means算法的数据选择框架,以从数据集中获得高精度的聚类。它与传统的k-均值型算法在三个方面不同。首先,在聚类学习过程中,我们将一个数据项合并到潜在聚类中产生的聚类的Bregman Information的变化值作为数据项聚类倾向的度量。其次,只选取聚类倾向强的数据项,即聚类的Bregman Information变化值小于预定义半径的数据项来学习聚类模式,其余数据点忽略,不属于聚类。聚类是非穷举的。第三,聚类的半径可以在学习过程中改变。这是一个动态的学习框架。在合成数据、文档数据和图像数据上的实验表明了该算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信