A clustering method for web data with multi-type interrelated components

Date

2007-05-08

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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Traditional clustering algorithms work on "flat" data, making the assumption that the data instances can only be represented by a set of homogeneous and uniform features. Many real world data, however, is heterogeneous in nature, comprising of multiple types of interrelated components. We present a clustering algorithm, K-SVMeans, that integrates the well known K-Means clustering with the highly popular Support Vector Machines(SVM) in order to utilize the richness of data. Our experimental results on authorship analysis of scientific publications show that K-SVMeans achieves better clustering performance than homogeneous data clustering.

URI

https://hdl.handle.net/11511/69643

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Department of Computer Engineering, Conference / Seminar