Grid-based clustering over an evolving data stream

Renxia Wan, Jingchao Chen, Lixin Wang and Xiaoke Su

International Journal of Data Mining, Modelling and Management, 2009, vol. 1, issue 4, 393-410

Abstract: Clustering on data stream has a great challenge because it has to be implemented within a limited space and a strict time constraint and the data stream may be potentially infinite. Fortunately, many clustering algorithms for data stream have been proposed, these algorithms have greatly promoted the clustering level of data stream, but most of the algorithms are designed for convex clusters. In this paper, a grid-based clustering algorithm is presented, it maps every data into the corresponding grid firstly and then iteratively merges these grids into clusters via merging steps, only boundary grids are considered during the merging stage. The algorithm also can group the evolving data stream into arbitrary shaped clusters. Compared with the same categorical algorithms, it has a less parameters input. In terms of effectivity and efficiency, the proposed algorithm outperforms the same categorical ones from theoretical and experimental analysis.

Keywords: clustering; data stream; grid clique; neighbouring grid; boundary grids; merging; acceptable distance; grid characteristic information; grid computing. (search for similar items in EconPapers)
Date: 2009
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=29033 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdmmm:v:1:y:2009:i:4:p:393-410

Access Statistics for this article

More articles in International Journal of Data Mining, Modelling and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().