NDLI: Discovering Clusters with Arbitrary Shapes and Densities in Data Streams

Content Provider	IEEE Xplore Digital Library
Author	Magdy, A. Yousri, N.A. El-Makky, N.M.
Copyright Year	2011
Abstract	The availability of streaming data in different fields and in various forms increases the importance of streaming data analysis. The huge size of a continuously flowing data has put forward a number of challenges in data stream analysis. Exploration of the structure of streamed data represented a major challenge that resulted in introducing various clustering algorithms. However, current clustering algorithms still lack the ability to efficiently discover clusters of arbitrary densities in data streams. In this paper, a new grid-based and density-based algorithm is proposed for clustering streaming data. It addresses drawbacks of recent algorithms in discovering clusters of arbitrary densities. The algorithm uses an online component to map the input data to grid cells. An offline component is then used to cluster the grid cells based on density information. Relative density relatedness measures and a dynamic range neighborhood are proposed to differentiate clusters of arbitrary densities. The experimental evaluation shows considerable improvements upon the state-of-the-art algorithms in both clustering quality and scalability. In addition, the output quality of the proposed algorithm is less sensitive to parameter selection errors.
Starting Page	279
Ending Page	282
File Size	470758
Page Count	4
File Format	PDF
ISBN	9781457721342
DOI	10.1109/ICMLA.2011.56
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-18
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Density measurement Shape density-based clustering Heuristic algorithms Scalability Clustering algorithms Data streams mining Partitioning algorithms
Content Type	Text
Resource Type	Article

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