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

Levent Bolelli; Seyda Ertekin; Ding Zhou; C. Lee Giles

doi:10.1145/1242572.1242725

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

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	16th International World Wide Web Conference, WWW2007
Pages	1121-1122
Number of pages	2
DOIs	https://doi.org/10.1145/1242572.1242725
State	Published - Oct 22 2007
Event	16th International World Wide Web Conference, WWW2007 - Banff, AB, Canada Duration: May 8 2007 → May 12 2007

Other

Other	16th International World Wide Web Conference, WWW2007
Country/Territory	Canada
City	Banff, AB
Period	5/8/07 → 5/12/07

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Software

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10.1145/1242572.1242725

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title = "A clustering method for web data with multi-type interrelated components",

abstract = "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.",

author = "Levent Bolelli and Seyda Ertekin and Ding Zhou and Giles, {C. Lee}",

year = "2007",

month = oct,

day = "22",

doi = "10.1145/1242572.1242725",

language = "English (US)",

isbn = "1595936548",

pages = "1121--1122",

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note = "16th International World Wide Web Conference, WWW2007 ; Conference date: 08-05-2007 Through 12-05-2007",

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T1 - A clustering method for web data with multi-type interrelated components

AU - Bolelli, Levent

AU - Ertekin, Seyda

AU - Zhou, Ding

AU - Giles, C. Lee

PY - 2007/10/22

Y1 - 2007/10/22

N2 - 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.

AB - 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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U2 - 10.1145/1242572.1242725

DO - 10.1145/1242572.1242725

M3 - Conference contribution

AN - SCOPUS:35348916642

SN - 1595936548

SN - 9781595936547

SP - 1121

EP - 1122

BT - 16th International World Wide Web Conference, WWW2007

T2 - 16th International World Wide Web Conference, WWW2007

Y2 - 8 May 2007 through 12 May 2007

ER -

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

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