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Clustering with Proximity Graphs: Exact and Efficient Algorithms

Clustering with Proximity Graphs: Exact and Efficient Algorithms

Michail Kazimianec, Nikolaus Augsten
Copyright: © 2013 |Volume: 3 |Issue: 4 |Pages: 21
ISSN: 2155-6393|EISSN: 2155-6407|EISBN13: 9781466635920|DOI: 10.4018/ijkbo.2013100105
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MLA

Kazimianec, Michail, and Nikolaus Augsten. "Clustering with Proximity Graphs: Exact and Efficient Algorithms." IJKBO vol.3, no.4 2013: pp.84-104. http://doi.org/10.4018/ijkbo.2013100105

APA

Kazimianec, M. & Augsten, N. (2013). Clustering with Proximity Graphs: Exact and Efficient Algorithms. International Journal of Knowledge-Based Organizations (IJKBO), 3(4), 84-104. http://doi.org/10.4018/ijkbo.2013100105

Chicago

Kazimianec, Michail, and Nikolaus Augsten. "Clustering with Proximity Graphs: Exact and Efficient Algorithms," International Journal of Knowledge-Based Organizations (IJKBO) 3, no.4: 84-104. http://doi.org/10.4018/ijkbo.2013100105

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Abstract

Graph Proximity Cleansing (GPC) is a string clustering algorithm that automatically detects cluster borders and has been successfully used for string cleansing. For each potential cluster a so-called proximity graph is computed, and the cluster border is detected based on the proximity graph. However, the computation of the proximity graph is expensive and the state-of-the-art GPC algorithms only approximate the proximity graph using a sampling technique. Further, the quality of GPC clusters has never been compared to standard clustering techniques like k-means, density-based, or hierarchical clustering. In this article the authors propose two efficient algorithms, PG-DS and PG-SM, for the exact computation of proximity graphs. The authors experimentally show that our solutions are faster even if the sampling-based algorithms use very small sample sizes. The authors provide a thorough experimental evaluation of GPC and conclude that it is very efficient and shows good clustering quality in comparison to the standard techniques. These results open a new perspective on string clustering in settings, where no knowledge about the input data is available.

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