K-SVMeans: A hybrid clustering algorithm for multi-type interrelated datasets

Date

2007-01-01

Author

Bolelli, Levent
Ertekin Bolelli, Şeyda
Zhou, Ding
Giles, C. Lee

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Identification of distinct clusters of documents in text collections has traditionally been addressed by making the assumption that the data instances can only be represented by homogeneous and uniform features. Many real-world data, on the other hand, comprise of multiple types of heterogeneous interrelated components, such as web pages and hyperlinks, online scientific publications and authors and publication venues to name a few. In this paper, we present K-SVMeans, a clustering algorithm for multi-type interrelated datasets that integrates the well known K-Means clustering with the highly popular Support Vector Machines. The experimental results on authorship analysis of two real world web-based datasets show that K-SVMeans can successfully discover topical clusters of documents and achieve better clustering solutions than homogeneous data clustering.

Subject Keywords

Clustering algorithms , Support vector machines, Web pages, Web sites, Support vector machine classification, Supervised learning, Computer science, Data engineering, Educational institutions, Information services

URI

https://hdl.handle.net/11511/45595

DOI

https://doi.org/10.1109/wi.2007.61

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

L. Bolelli, Ş. Ertekin Bolelli, D. Zhou, and C. L. Giles, “K-SVMeans: A hybrid clustering algorithm for multi-type interrelated datasets,” 2007, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/45595.