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A fusion approach to cluster labeling

Authors:

Michal Shmueli-ScheuerAuthors Info & Claims

SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

Pages 883 - 886

https://doi.org/10.1145/2600428.2609465

Published: 03 July 2014 Publication History

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Abstract

We present a novel approach to the cluster labeling task using fusion methods. The core idea of our approach is to weigh labels, suggested by any labeler, according to the estimated labeler's decisiveness with respect to each of its suggested labels. We hypothesize that, a cluster labeler's labeling choice for a given cluster should remain stable even in the presence of a slightly incomplete cluster data. Using state-of-the-art cluster labeling and data fusion methods, evaluated over a large data collection of clusters, we demonstrate that, overall, the cluster labeling fusion methods that further consider the labeler's decisiveness provide the best labeling performance.

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Cited By

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Penta APal A(2021)What is this Cluster about? Explaining textual clusters by extracting relevant keywordsKnowledge-Based Systems10.1016/j.knosys.2021.107342229:COnline publication date: 11-Oct-2021
https://dl.acm.org/doi/10.1016/j.knosys.2021.107342
Das PDas A(2019)Rough set based incremental crime report labelling in dynamic environmentApplied Soft Computing10.1016/j.asoc.2019.105811(105811)Online publication date: Oct-2019
https://doi.org/10.1016/j.asoc.2019.105811

Index Terms

A fusion approach to cluster labeling
1. Information systems
  1. Information retrieval

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Published In

SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

July 2014

1330 pages

ISBN:9781450322577

DOI:10.1145/2600428

General Chairs:
Shlomo Geva
Queensland University of Technology
,
Andrew Trotman
University of Dunedin
,
Program Chairs:
Peter Bruza
Queensland University of Technology
,
Charles L.A. Clarke
University of Waterloo
,
Kal Järvelin
University of Tampere

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 July 2014

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SIGIR '14

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SIGIR

SIGIR '14: The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 6 - 11, 2014

Queensland, Gold Coast, Australia

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SIGIR '14 Paper Acceptance Rate 82 of 387 submissions, 21%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

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Penta APal A(2021)What is this Cluster about? Explaining textual clusters by extracting relevant keywordsKnowledge-Based Systems10.1016/j.knosys.2021.107342229:COnline publication date: 11-Oct-2021
https://dl.acm.org/doi/10.1016/j.knosys.2021.107342
Das PDas A(2019)Rough set based incremental crime report labelling in dynamic environmentApplied Soft Computing10.1016/j.asoc.2019.105811(105811)Online publication date: Oct-2019
https://doi.org/10.1016/j.asoc.2019.105811

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Automatically labeling hierarchical clusters

Analysis of structural relationships for hierarchical cluster labeling

Enhancing cluster labeling using wikipedia