Clustering over Evolving Data Streams Basedon Online Recent-Biased Approximation

机译：基于在线最近偏差近似的演化数据流聚类

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A growing number of real world applications deal with multiple evolving data streams. In this paper, a framework for clustering over evolving data streams is proposed taking advantage of recent-biased approximation. In recent-biased approximation, more details are preserved for recent data and fewer coefficients are kept for the whole data stream, which improves the efficiency of clustering and space usability greatly. Our framework consists of two phases. One is an online phase which approximates data streams and maintains the summary statistics incrementally. The other is an offline clustering phase which is able to perform dynamic clustering over data streams on all possible time horizons. As shown in complexity analyses and also validated by our empirical studies, our framework performed efficiently in the data stream environment while producing clustering results of very high quality.

机译：越来越多的现实应用程序处理多个不断发展的数据流。在本文中，提出了利用最近偏逼近法对不断发展的数据流进行聚类的框架。在最近偏近似中，为最新数据保留了更多细节，为整个数据流保留了较少的系数，这极大地提高了聚类效率和空间可用性。我们的框架包括两个阶段。一个是在线阶段，它近似数据流并逐步维护摘要统计信息。另一个是脱机集群阶段，它能够在所有可能的时间范围内对数据流执行动态集群。如复杂性分析所示，并通过我们的经验研究验证，我们的框架在数据流环境中有效执行，同时产生非常高质量的聚类结果。

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来源
《Knowledge Acquisition : Approaches, algorithms and applications》|2008年|P.12-26|共15页
会议地点 Hanoi(VN);Hanoi(VN)
作者
Wei Fan; rnYusuke Koyanagi; rnKoichi Asakura; rnToyohide Watanabe;
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作者单位

Department of Systems and Social Informatics,Graduate School of Information Science, Nagoya University Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan;

rnDepartment of Systems and Social Informatics,Graduate School of Information Science, Nagoya University Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan;

rnSchool of Informatics, Daido Institute of Technology 10-3 Takiharu-cho, Minami-ku, Nagoya, 457-8530, Japan;

rnDepartment of Systems and Social Informatics,Graduate School of Information Science, Nagoya University Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan;

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原文格式 PDF
正文语种 eng
中图分类信息处理（信息加工）;
关键词
clustering over evolving data streams; time series data; recent-biased approximation; data mining;

机译：在不断发展的数据流上集群；时间序列数据；最近偏的近似数据挖掘;

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专利

1. A buffer-based online clustering for evolving data stream [J] . Islam Md. Kamrul, Ahmed Md. Manjur, Zamli Kamal Z. Information Sciences: An International Journal . 2019,第期

机译：基于缓冲的在线群集，用于不断发展数据流
2. An evolving connectionist system for data stream fuzzy clustering and its online learning [J] . Bodyanskiy Yevgeniy V., Tyshchenko Oleksii K., Kopaliani Daria S. Neurocomputing . 2017,第nova1期

机译：不断发展的连接器系统，用于数据流模糊聚类及其在线学习
3. MuDi-Stream: A multi density clustering algorithm for evolving data stream [J] . Amini Amineh, Saboohi Hadi, Herawan Tutut, Journal of network and computer applications . 2016,第JANa期

机译：MuDi-Stream：用于发展数据流的多密度聚类算法
4. Clustering over Evolving Data Streams Based on Online Recent-Biased Approximation [C] . Wei Fan, Yusuke Koyanagi, Koichi Asakura, Pacific Rim Knowledge Acquisition Workshop . 2009

机译：通过基于在线近期偏见的近似的演化数据流聚类
5. Approximation algorithms for clustering streams and large data sets. [D] . O'Callaghan, Liadan Ita. 2003

机译：用于对流和大数据集进行聚类的近似算法。
6. Cost-Sensitive Classification for Evolving Data Streams with Concept Drift and Class Imbalance [O] . Yange Sun, Meng Li, Lei Li, 2021

机译：具有与概念漂移和类不平衡的演化数据流的成本敏感分类
7. Fully online clustering of evolving data streams into arbitrarily shaped clusters [O] . Hyde, Richard, Angelov, Plamen, MacKenzie, A. R. 2017

机译：将不断发展的数据流完全在线群集为任意形状的群集

Clustering over Evolving Data Streams Basedon Online Recent-Biased Approximation

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