Some Challenges in Clustering Analysis

Jiang Sheng-yi; Wang Lian-xi

doi:10.3969/j.issn.1007-7162.2014.03.006

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Some Challenges in Clustering Analysis

更新时间：2026-05-27

- Some Challenges in Clustering Analysis
- Vol. 31, Issue 3, Pages: 32-38(2014)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1007-7162.2014.03.006
  CLC：
- Online First：30 September 2014，
  
  Published：2014
- 稿件说明：
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蒋盛益, 王连喜. 聚类分析研究的挑战性问题[J]. 广东工业大学学报, 2014, 31(3): 32-38.

Jiang Sheng-yi, Wang Lian-xi. Some Challenges in Clustering Analysis[J]. 2014, 31(3): 32-38.
蒋盛益, 王连喜. 聚类分析研究的挑战性问题[J]. 广东工业大学学报, 2014, 31(3): 32-38. DOI： 10.3969/j.issn.1007-7162.2014.03.006.

Jiang Sheng-yi, Wang Lian-xi. Some Challenges in Clustering Analysis[J]. 2014, 31(3): 32-38. DOI： 10.3969/j.issn.1007-7162.2014.03.006.

摘要

聚类的目的是帮助人们发现和认识未知世界，为现实生活中的学习积累知识.聚类分析一直是广大学者重点关注的无监督学习内容，也是许多交叉学科用来探索数据中潜在规律的重要分析工具.通过简单梳理聚类分析的研究成果，在理解聚类分析基本框架的基础上对当前聚类算法在处理多样化数据类型的能力、处理超高维数据的能力、处理不均衡数据的能力、算法的可拓展能力、效果评价的指标选择问题等方面出现的挑战性问题进行了论述，并分析了未来有待重点解决的一些问题.这些工作将为后续聚类分析和数据挖掘的深入研究提供有价值的参考.

Abstract

The aim of clustering is to help people find and recognize the unknown world

so as to accumulate knowledge for us in real life. Clustering analysis is an important part for the majority of researchers in unsupervised leaning

and is usually used as an analysis tool to explore the unknown data and its regularity for many cross subjects. It analyzed the procedure of clustering

and briefly surveyed the related achievements. Moreover

the problems of clustering algorithms in processing various data types

high dimensional data

unbalanced data were concluded

and the expansibility and the selection of evaluation index for algorithms were also discussed in detail. At last

some directions for future research were proposed. The above work can give valuable reference to further studies of clustering and data mining.

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