一种基于仿射传播的增强型流聚类算法

赵建龙 1; 曲桦 1; 2; 赵季红 2; 蒋丁潮 2

doi:10.7652/xjtuxb201703018

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一种基于仿射传播的增强型流聚类算法

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- 一种基于仿射传播的增强型流聚类算法
- An Enhanced Stream Clustering Algorithm Based on Affinity Propagation
- 西安交通大学学报 2017年51卷第3期页码：105-110
- 作者机构：
  
  西安交通大学软件学院,西安,710049
- 作者简介：
- 基金信息：
  
  : 2016-05-28。作者简介: 赵建龙(1992—),男,博士生;曲桦(通信作者),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(61371087,61531013);国家“863计划”资助项目(2015AA015702)
- DOI：10.7652/xjtuxb201703018
  中图分类号： TP181
- 网络首发：2017-03-10，
  
  纸质出版：2017
- 稿件说明：
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赵建龙 1, 曲桦 1, 2, 等. 一种基于仿射传播的增强型流聚类算法[J]. 西安交通大学学报, 2017,51(3):105-110.

An Enhanced Stream Clustering Algorithm Based on Affinity Propagation[J]. 2017, 51(3): 105-110.
赵建龙 1, 曲桦 1, 2, 等. 一种基于仿射传播的增强型流聚类算法[J]. 西安交通大学学报, 2017,51(3):105-110. DOI： 10.7652/xjtuxb201703018.

An Enhanced Stream Clustering Algorithm Based on Affinity Propagation[J]. 2017, 51(3): 105-110. DOI： 10.7652/xjtuxb201703018.

摘要

针对目前流聚类算法无法有效处理数据流离群点的检测和处理

以及增量式数据流聚类效率较低等问题

提出了一种基于密度度量的异常检测、删除的增强型仿射传播流聚类算法。在仿射传播流聚类算法的基础上

所提算法通过引进异常检测和删除机制改善了异常点对聚类精度、聚类效率的影响。利用仿射传播聚类实现在线数据流的聚类过程

同时检测数据漂移现象

即数据流分布特征随时间发生变化

并采用基于密度度量的局部异常因子检测技术(LOF)对储备池数据进行异常检测和删除处理

通过对当前类簇和处理过的储备池数据重聚类来重建动态数据流模型。在真实网络数据(KDD'99)上进行了实验

结果表明

所提算法不仅减少了重聚类构建动态模型的次数

改善了聚类效率

而且在同时考虑聚类精度、纯度和熵3种聚类评价标准下

均优于传统的仿射传播流聚类算法。

Abstract

Aiming at the problem that the traditional stream clustering algorithm cannot effectively deal with the inspection and treatment of outliers

and the incremental data stream clustering efficiency is low

an enhanced stream clustering algorithm based on affinity propagation using density measurement was proposed. Based on the STRAP

the proposed algorithm can improve the clustering accuracy and efficiency by introducing a mechanism for outlier detection and removal. Firstly

the online stream clustering process is realized by the affinity propagation algorithm. Meanwhile

the phenomenon of data drift is detected

i.e.

the distribution of data stream changes with time. In view of this phenomenon

the new algorithm can implement the outlier detection and removal in the reservoir based on local outlier factor

and then re-cluster the current cluster and the treated reservoir to reconstruct the dynamic stream clustering model. Finally

through the validation on the KDD'99 data

the experimental results showed that the proposed algorithm not only reduces the number of re-clustering and improves the clustering efficiency

but also is superior to the STRAP in terms of the three clustering evaluation criteria

i.e.

the clustering accuracy

purity and entropy.

关键词

Keywords

references

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ZHOU Aoying, CAO Feng, QIAN Weining, et al. Tracking clusters in evolving data streams over sliding windows [J]. Knowledge and Information Systems, 2008, 15(2): 181-214.

ACKERMANN M R, MARTENS M, RAUPACH C, et al. StreamKM++: a clustering algorithm for data streams [J]. Journal of Experimental Algorithmics, 2012, 17: 173-187.

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ZHANG Xiangliang, FURTLEHNER C, SEBAG M. Data streaming with affinity propagation [C]∥ Lecture Notes in Computer Science. Berlin, Germany: Springer, 2008: 628-643.

ZHANG Xiangliang, FURTLEHNER C, GERMAIN-RENAUD C, et al. Data stream clustering with affinity propagation [J]. IEEE Transactions on Knowledge Data Engineering, 2014, 26(7): 1644-1656.

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BREUNIG M M, KRIEGEL H P, NG R T, et al. LOF: identifying density-based local outliers [J]. ACM Sigmod Record, 2000, 29(2): 93-104.

WU Junjie, HUI Xiong, CHEN Jian. Adapting the right measures for K-means clustering [C]∥Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2009: 877-886.

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