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MOSAIC: Agglomerative Clustering with Gabriel Graphs

MOSAIC: Agglomerative Clustering with Gabriel Graphs

Rachsuda Jiamthapthaksin, Jiyeon Choo, Chun-sheng Chen, Oner Ulvi Celepcikay, Christian Giusti, Christoph F. Eick
Copyright: © 2010 |Pages: 20
ISBN13: 9781605667485|ISBN10: 160566748X|ISBN13 Softcover: 9781616924522|EISBN13: 9781605667492
DOI: 10.4018/978-1-60566-748-5.ch010
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MLA

Jiamthapthaksin, Rachsuda, et al. "MOSAIC: Agglomerative Clustering with Gabriel Graphs." Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications, edited by Tho Manh Nguyen, IGI Global, 2010, pp. 231-250. https://doi.org/10.4018/978-1-60566-748-5.ch010

APA

Jiamthapthaksin, R., Choo, J., Chen, C., Celepcikay, O. U., Giusti, C., & Eick, C. F. (2010). MOSAIC: Agglomerative Clustering with Gabriel Graphs. In T. Nguyen (Ed.), Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications (pp. 231-250). IGI Global. https://doi.org/10.4018/978-1-60566-748-5.ch010

Chicago

Jiamthapthaksin, Rachsuda, et al. "MOSAIC: Agglomerative Clustering with Gabriel Graphs." In Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications, edited by Tho Manh Nguyen, 231-250. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-748-5.ch010

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Abstract

Strong theoretical foundation and low computational complexity make representative-based clustering one of the most popular approaches for a clustering problem. Despite those superiorities, it presents two main drawbacks: the shape of clusters obtained is limited to convex shapes, and its performance is highly dependent on seeds initialization. To address these problems, the authors introduce MOSAIC, a novel agglomerative clustering algorithm, which greedily merges neighboring clusters maximizing a plug-in fitness function. The key idea is that by considering neighboring relationship computed using Gabriel Graphs among cluster, MOSAIC can derive non-convex shapes as the unions of small clusters previously generated by a representative-based clustering algorithm. The authors evaluate MOSAIC for traditional unsupervised clustering with k-means and DBSCAN, and also for supervised clustering. The experimental results show that compared to k-means stand-alone, their proposed post-processing techniques obtain higher quality clusters, whereas compared to DBSCAN results, MOSAIC is capable of identifying comparable arbitrary shape clusters, given a suitable fitness function. In addition, MOSAIC can cope with problems of clustering on high dimensional data. The authors also claim that MOSAIC can be employed as an effective post-processing clustering algorithm to further improve the quality of clustering.

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