优化子空间的高维聚类算法

doi:10.11772/j.issn.1001-9081.2014.08.2279

计算机应用 ›› 2014, Vol. 34 ›› Issue (8): 2279-2284.DOI: 10.11772/j.issn.1001-9081.2014.08.2279

优化子空间的高维聚类算法

吴涛,陈黎飞,郭躬德

福建师范大学数学与计算机科学学院，福州350007

收稿日期:2014-01-06 修回日期:2014-04-04 发布日期:2014-08-10 出版日期:2014-08-01
通讯作者: 吴涛
作者简介:吴涛(1990-)，男，福建龙岩人，硕士研究生，主要研究方向：数据挖掘；陈黎飞(1972-)，男，福建长乐人，副教授，博士，主要研究方向：数据挖掘、机器学习；郭躬德(1965-)，男，福建龙岩人，教授，博士，主要研究方向：人工智能、数据挖掘、机器学习。
基金资助:
国家自然科学基金资助项目;深圳市基础研究（重点）项目

High-dimensional data clustering algorithm with subspace optimization

WU Tao,CHEN Lifei,GUO Gongde

School of Mathematics and Computer Science, Fujian Normal University, Fuzhou Fujian 350007, China

Received:2014-01-06 Revised:2014-04-04 Online:2014-08-10 Published:2014-08-01
Contact: WU Tao

摘要/Abstract

摘要：

针对当前大多数典型软子空间聚类算法未能考虑簇类投影子空间的优化问题，提出一种新的软子空间聚类算法。该算法将最大化权重之间的差异性作为子空间优化的目标，并提出了一个量化公式。以此为基础设计了一个新的优化目标函数，在最小化簇内紧凑度的同时，优化每个簇所在的软子空间。通过数学推导得到了新的特征权重计算方法，并基于k-means算法框架定义了新聚类算法。实验结果表明，所提算法对子空间的优化降低了算法过早陷入局部最优的可能性，提高了算法的稳定性，并且具有良好的性能和聚类效果，适合用于高维数据聚类分析。

Abstract:

A new soft subspace clustering algorithm was proposed to address the optimization problem for the projected subspaces, which was generally not considered in most of the existing soft subspace clustering algorithms. Maximizing the deviation of feature weights was proposed as the sub-space optimization goal, and a quantitative formula was presented. Based on the above, a new optimization objective function was designed which aimed at minimizing the within-cluster compactness while optimizing the soft subspace associated with each cluster. A new expression for feature-weight computation was mathematically derived, with which the new clustering algorithm was defined based on the framework of the classical k-means. The experimental results show that the proposed method significantly reduces the probability of trapping in local optimum prematurely and improves the stability of clustering results. And it has good performance and clustering efficiency, which is suitable for high-dimensional data cluster analysis.

中图分类号:

TP181

吴涛陈黎飞郭躬德. 优化子空间的高维聚类算法[J]. 计算机应用, 2014, 34(8): 2279-2284.

WU Tao CHEN Lifei GUO Gongde. High-dimensional data clustering algorithm with subspace optimization[J]. Journal of Computer Applications, 2014, 34(8): 2279-2284.

参考文献

［1］HAN J, KAMBER M. Data mining: concepts and techniques ［M］. FAN M, MENG X, translated. 2nd ed. Beijing: China Machine Press, 2007.(HAN J, KAMBER M.数据挖掘:概念与技术［M］.范明,孟小峰,译.2版.北京:机械工业出版社,2007.)
［2］JAIN A, MURTY M, FLYNN P J. Data clustering: a review ［J］. ACM Computing Surveys, 1999, 31(3): 264-323.
［3］LEOPOLD E, KINDERMANN J. Text categorization with support vector machines: how to represent texts in input space? ［J］. Machine Learning, 2002, 46(1/2/3): 423-444.
［4］CHEN L. Research on clustering methods for high dimensional data and their applications ［D］. Xiamen: Xiamen University, 2008.(陈黎飞.高维数据的聚类方法研究与应用［D］.厦门：厦门大学,2008.)
［5］PARSONS L, HAQUE E, LIU H. Subspace clustering for high dimensional data: a review ［J］. ACM Knowledge Discovery and Data Mining Explorations Newsletter, 2004, 6(1): 90-105.
［6］VERLEYSEN M. Learning high-dimensional data ［C］// Proceedings of the Limitations and Future Trends in Neural Computation. Siena: IOS Press, 2003:141-162.
［7］YANG Q, WU X. 10 challenging problems in data mining research ［J］. International Journal of Information Technology and Decision Making, 2006, 5(4): 597-604.
［8］KRIEGEL H P, KRGER P, ZIMEK A. Clustering high-dimen-sional data: a survey on subspace clustering, pattern-based clustering, and correlation clustering ［J］. ACM Transactions on Knowledge Discovery from Data, 2009, 3(1): 1-58.
［9］JING L, NG M K, HUANG J Z. An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data ［J］. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(8): 1026-1041.
［10］HUANG J Z, NG M K, RONG H, et al. Automated variable weighting in k-means type clustering ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(5): 657-668.
［11］DOMENICONI C, GUNOPULOS D, MA S, et al. Locally adaptive metrics for clustering high dimensional data ［J］. Data Mining and Knowledge Discovery, 2007, 14(1): 63-97.
［12］GAO G, WU J, YANG Z. A fuzzy subspace clustering algorithm for clustering high dimensional data ［C］// Proceedings of the Second International Conference on Advanced Data Mining and Applications. Berlin: Springer, 2006: 271-278.
［13］XU L, JORDAN M I. On convergence properties of the EM algorithm for Gaussian mixtures ［J］. Neural Computation, 1996, 8(1): 129-151.
［14］GULLO F, DOMENICONI C, TAGARELLI A. Projective clustering ensembles ［J］. Data Mining and Knowledge Discovery, 2013, 26(3): 452-511.
［15］CHEN L, GUO G, JIANG Q. An adaptive algorithm for soft subspace clustering ［J］. Journal of Software, 2010, 21(10): 2513-2523.(陈黎飞,郭躬德,姜青山.自适应的软子空间聚类算法［J］.软件学报,2010,21(10):2513-2523.)
［16］DENG Z, CHOI K S, CHUNG F L, et al. Enhanced soft subspace clustering integrating within-cluster and between-cluster information ［J］. Pattern Recognition, 2010, 43(3): 767-781.
［17］CHEN L, JIANG Q, WANG S. A probability model for projective clustering on high dimensional data ［C］// ICDM'08: Proceedings of the Eighth IEEE International Conference on Data Mining. Washington, DC: IEEE Computer Society, 2008: 755-760.
［18］XUE Y. Optimization theory and method ［M］. Beijing: Beijing University of Technology Press, 2001.(薛毅.最优化原理与方法［M］.北京:北京工业大学出版社,2001.)
［19］ZHAO Y, KARYPIS G. Comparison of agglomerative and partitional document clustering algorithms, TR 02-014 ［R］. Minneapolis: University of Minnesota, 2002.
［20］STREHL A, GHOSH J. Cluster ensembles—a knowledge reuse framework for combining multiple partitions ［J］. The Journal of Machine Learning Research, 2003, 3: 583-617.

[1]	宫智宇王士同. 面向重尾噪声图像分类的残差网络学习方法[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[2]	王虎王晓峰李可马云洁. 融合多头自注意力的标签语义嵌入联邦类增量学习方法[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[3]	刘晶鑫, 黄雯静, 徐亮胜, 黄冲, 吴建生. 字典学习与样本关联保持结合的无监督特征选择模型[J]. 《计算机应用》唯一官方网站, 2024, 44(12): 3766-3775.
[4]	宋逸飞, 柳毅. 基于数据增强和标签噪声的快速对抗训练方法[J]. 《计算机应用》唯一官方网站, 2024, 44(12): 3798-3807.
[5]	赵小阳许新征李仲年. 物联网应用中的可解释人工智能研究综述[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[6]	余家宸, 杨晔. 基于裁剪近端策略优化算法的软机械臂不规则物体抓取[J]. 《计算机应用》唯一官方网站, 2024, 44(11): 3629-3638.
[7]	黄雨鑫, 黄贻望, 黄辉. 基于浅层网络预测的元标签校正方法[J]. 《计算机应用》唯一官方网站, 2024, 44(11): 3364-3370.
[8]	李志杰, 廖旭红, 李元香, 李青蓝. 基于基因关联分析的贝叶斯网络疾病样本分类算法[J]. 《计算机应用》唯一官方网站, 2024, 44(11): 3449-3458.
[9]	胡婕郑启扬孙军张龑. 基于多关系标签图和局部动态重构学习的多标签分类模型[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[10]	柴汶泽, 范菁, 孙书魁, 梁一鸣, 刘竟锋. 深度度量学习综述[J]. 《计算机应用》唯一官方网站, 2024, 44(10): 2995-3010.
[11]	尹春勇, 周永成. 双端聚类的自动调整聚类联邦学习[J]. 《计算机应用》唯一官方网站, 2024, 44(10): 3011-3020.
[12]	曹锋, 杨小玲, 易见兵, 李俊. 矛盾体分离超演绎方法及应用[J]. 《计算机应用》唯一官方网站, 2024, 44(10): 3074-3080.
[13]	许鹏程何磊李川钱炜祺赵暾. 基于Transformer的深度符号回归方法[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[14]	颜文婧王瑞东左敏张青川. 基于风味嵌入异构图层次学习的食谱推荐模型[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[15]	郭书剑余节约尹学松. 图正则化弹性网子空间聚类[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.

优化子空间的高维聚类算法

High-dimensional data clustering algorithm with subspace optimization

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics