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Scatter/Gather: a cluster-based approach to browsing large document collections

Authors:

Douglass R. Cutting,

David R. Karger,

Jan O. Pedersen,

John W. TukeyAuthors Info & Claims

SIGIR '92: Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 318 - 329

https://doi.org/10.1145/133160.133214

Published: 01 June 1992 Publication History

Abstract

Document clustering has not been well received as an information retrieval tool. Objections to its use fall into two main categories: first, that clustering is too slow for large corpora (with running time often quadratic in the number of documents); and second, that clustering does not appreciably improve retrieval.

We argue that these problems arise only when clustering is used in an attempt to improve conventional search techniques. However, looking at clustering as an information access tool in its own right obviates these objections, and provides a powerful new access paradigm. We present a document browsing technique that employs document clustering as its primary operation. We also present fast (linear time) clustering algorithms which support this interactive browsing paradigm.

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Cited By

Gokilapriya SSuganya RPrema RSarika S(2024)Applying Data Mining Techniques for Phrase Extraction in Document CollectionsComputer Science, Engineering and Technology10.46632/cset/2/3/62:3(44-46)Online publication date: 6-Sep-2024
https://doi.org/10.46632/cset/2/3/6
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https://dl.acm.org/doi/10.1145/3613904.3642858
Fok RLipka NSun TSiu A(2024)Marco: Supporting Business Document Workflows via Collection-Centric Information Foraging with Large Language ModelsProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3641969(1-20)Online publication date: 11-May-2024
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Index Terms

Scatter/Gather: a cluster-based approach to browsing large document collections
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
    2. Retrieval models and ranking
2. Theory of computation
  1. Design and analysis of algorithms

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Scatter/Gather: A Cluster-based Approach to Browsing Large Document Collections
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Published In

cover image ACM Conferences

SIGIR '92: Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval

June 1992

352 pages

ISBN:0897915232

DOI:10.1145/133160

Chairman:
Edward Fox
VPI & State Univ., Blacksburg, VA
,
Editors:
Nicholas Belkin
Rutgers Univ., New Brunswick, NJ
,
Peter Ingwersen,
Annelise Mark Pejtersen

Copyright © 1992 ACM.

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]

Sponsors

SIGIR: ACM Special Interest Group on Information Retrieval
Royal School of Lib.: Royal School of Lib.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 June 1992

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SIGIR92: 15th International ACM/SIGIR Conference on Research & Development in Information Retrieval

June 21 - 24, 1992

Copenhagen, Denmark

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

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https://doi.org/10.46632/cset/2/3/6
Almeda SZamfirescu-Pereira JKim KMani Rathnam PHartmann B(2024)Prompting for Discovery: Flexible Sense-Making for AI Art-Making with DreamsheetsProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642858(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642858
Fok RLipka NSun TSiu A(2024)Marco: Supporting Business Document Workflows via Collection-Centric Information Foraging with Large Language ModelsProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3641969(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3641969
Zhang WYan RYuan L(2024)How Generative AI Was Mentioned in Social Media and Academic Field? A Text Mining Based on Internet Text DataIEEE Access10.1109/ACCESS.2024.337901012(43940-43947)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3379010
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Amiri BKarimianghadim R(2024)A novel text clustering model based on topic modelling and social network analysisChaos, Solitons & Fractals10.1016/j.chaos.2024.114633181(114633)Online publication date: Apr-2024
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Suh SMin BPalani SXia H(2023)Sensecape: Enabling Multilevel Exploration and Sensemaking with Large Language ModelsProceedings of the 36th Annual ACM Symposium on User Interface Software and Technology10.1145/3586183.3606756(1-18)Online publication date: 29-Oct-2023
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Bascur JVerberne Svan Eck NWaltman L(2023)Academic information retrieval using citation clusters: in-depth evaluation based on systematic reviewsScientometrics10.1007/s11192-023-04681-x128:5(2895-2921)Online publication date: 21-Mar-2023
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Sadegh T(2022)Leveraging Regulative Learning Facilitators to Foster Student Agency and Knowledge (Co-)Construction Activities in CSCL EnvironmentsInternational Journal of Online Pedagogy and Course Design10.4018/IJOPCD.29320912:1(1-15)Online publication date: 21-Jan-2022
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Arslan GMadran USoyoğlu D(2022)An Algebraic Approach to Clustering and Classification with Support Vector MachinesMathematics10.3390/math1001012810:1(128)Online publication date: 1-Jan-2022
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