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

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


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Citation Formats
L. Bolelli, Ş. Ertekin Bolelli, D. Zhou, and C. L. Giles, “A clustering method for web data with multi-type interrelated components,” 2007, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/69643.