A framework for clustering evolving data streams

Charu C. Aggarwal, Jiawei Han, Jianyong Wang, Philip S. Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The clustering problem is a difficult problem for the data stream domain. This is because the large volumes of data arriving in a stream renders most traditional algorithms too inefficient. In recent years, a few one-pass clustering algorithms have been developed for the data stream problem. Although such methods address the scalability issues of the clustering problem, they are generally blind to the evolution of the data and do not address the following issues: (1) The quality of the clusters is poor when the data evolves considerably over time. (2) A data stream clustering algorithm requires much greater functionality in discovering and exploring clusters over different portions of the stream. The widely used practice of viewing data stream clustering algorithms as a class of onepass clustering algorithms is not very useful from an application point of view. For example, a simple one-pass clustering algorithm over an entire data stream of a few years is dominated by the outdated history of the stream. The exploration of the stream over different time windows can provide the users with a much deeper understanding of the evolving behavior of the clusters. At the same time, it is not possible to simultaneously perform dynamic clustering over all possible time horizons for a data stream of even moderately large volume. This paper discusses a fundamentally different philosophy for data stream clustering which is guided by application-centered requirements. The idea is divide the clustering process into an online component which periodically stores detailed summary statistics and an offline component which uses only this summary statistics. The offline component is utilized by the analyst who can use a wide variety of inputs (such as time horizon or number of clusters) in order to provide a quick understanding of the broad clusters in the data stream. The problems of efficient choice, storage, and use of this statistical data for a fast data stream turns out to be quite tricky. For this purpose, we use the concepts of a pyramidal time frame in conjunction with a microclustering approach. Our performance experiments over a number of real and synthetic data sets illustrate the effectiveness, efficiency, and insights provided by our approach.

Original languageEnglish (US)
Title of host publicationProceedings - 29th International Conference on Very Large Data Bases, VLDB 2003
EditorsPatricia G. Selinger, Michael J. Carey, Johann Christoph Freytag, Serge Abiteboul, Peter C. Lockemann, Andreas Heuer
PublisherMorgan Kaufmann
Number of pages12
ISBN (Electronic)0127224424, 9780127224428
StatePublished - 2003
Event29th International Conference on Very Large Data Bases, VLDB 2003 - Berlin, Germany
Duration: Sep 9 2003Sep 12 2003

Publication series

NameProceedings - 29th International Conference on Very Large Data Bases, VLDB 2003


Other29th International Conference on Very Large Data Bases, VLDB 2003

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Information Systems and Management
  • Computer Science Applications
  • Computer Networks and Communications


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