Article

Multiobjective clustering with automatic k-determination for large-scale data

Authors:

Nobukazu Matake,

Tomoyuki Hiroyasu,

Mitsunori Miki,

Tomoharu SendaAuthors Info & Claims

GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation

Pages 861 - 868

https://doi.org/10.1145/1276958.1277126

Published: 07 July 2007 Publication History

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Abstract

Web mining - data mining for web data - is a key factor of web technologies. Especially, web behavior mining has attracted a great deal of attention recently. Behavior mining involves analyzing the behavior of users, finding patterns of user behavior, and predicting their subsequent behaviors or interests. Web behavior mining is used in web advertising systems or content recommendation systems. To analyze huge amounts of data, such as web data, data-clustering techniques are usually used. Data clustering is a technique involving the separation of data into groups according to similarity, and is usually used in the first step of data mining. In the present study, we developed a scalable data-clustering algorithm for web mining based on existent evolutionary multiobjective clustering algorithm. To derive clusters, we applied multiobjective clustering with automatic k-determination (MOCK). It has been reported that MOCK shows better performance than k-means, agglutination methods, and other evolutionary clustering algorithms. MOCK can also find the appropriate number of clusters using the information of the trade-off curve. The k-determination scheme of MOCK is powerful and strict. However the computational costs are too high when applied to clustering huge data. In this paper, we propose a scalable automatic k-determination scheme. The proposed scheme reduces Pareto-size and the appropriate number of clusters can usually be determined.

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Morimoto CPozo Ade Souto M(2024)An adaptive evolutionary multi-objective clustering based on the data properties of the base partitionsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.123102245:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.123102
Dey ABhattacharyya SDey SKonar DPlatos JSnasel VMrsic LPal P(2023)A Review of Quantum-Inspired Metaheuristic Algorithms for Automatic ClusteringMathematics10.3390/math1109201811:9(2018)Online publication date: 24-Apr-2023
https://doi.org/10.3390/math11092018
Ren LYu GWang JLiu LDomeniconi CZhang X(2023)A Diversified Attention Model for Interpretable Multiple ClusteringsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.321869335:9(8852-8864)Online publication date: 1-Sep-2023
https://doi.org/10.1109/TKDE.2022.3218693
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Index Terms

Multiobjective clustering with automatic k-determination for large-scale data
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction
  2. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

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Published In

GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation

July 2007

2313 pages

ISBN:9781595936974

DOI:10.1145/1276958

General Chair:
Hod Lipson
Cornell University, USA

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

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Published: 07 July 2007

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GECCO07: Genetic and Evolutionary Computation Conference

July 7 - 11, 2007

London, England

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Morimoto CPozo Ade Souto M(2024)An adaptive evolutionary multi-objective clustering based on the data properties of the base partitionsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.123102245:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.123102
Dey ABhattacharyya SDey SKonar DPlatos JSnasel VMrsic LPal P(2023)A Review of Quantum-Inspired Metaheuristic Algorithms for Automatic ClusteringMathematics10.3390/math1109201811:9(2018)Online publication date: 24-Apr-2023
https://doi.org/10.3390/math11092018
Ren LYu GWang JLiu LDomeniconi CZhang X(2023)A Diversified Attention Model for Interpretable Multiple ClusteringsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.321869335:9(8852-8864)Online publication date: 1-Sep-2023
https://doi.org/10.1109/TKDE.2022.3218693
Yang QHan GGao WYang ZZhu SDeng Y(2023)A Robust Learning Membership Scaling Fuzzy C-Means Algorithm Based on New Belief PeakIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2023.328691031:12(4486-4500)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1109/TFUZZ.2023.3286910
Handl JGarza-Fabre MJosé-García A(2023)Evolutionary Clustering and Community DetectionHandbook of Evolutionary Machine Learning10.1007/978-981-99-3814-8_6(151-169)Online publication date: 2-Nov-2023
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Garza-Fabre MSanchez-Martinez AAldana-Bobadilla ELanda R(2022)Decision Making in Evolutionary Multiobjective Clustering: A Machine Learning ChallengeIEEE Access10.1109/ACCESS.2022.321985410(117281-117303)Online publication date: 2022
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Morimoto CPozo Ade Souto M(2022)An analysis of the admissibility of the objective functions applied in evolutionary multi-objective clusteringInformation Sciences: an International Journal10.1016/j.ins.2022.08.045610:C(1143-1162)Online publication date: 1-Sep-2022
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Aljarah IHabib MNujoom RFaris HMirjalili S(2021)A Comprehensive Review of Evaluation and Fitness Measures for Evolutionary Data ClusteringEvolutionary Data Clustering: Algorithms and Applications10.1007/978-981-33-4191-3_2(23-71)Online publication date: 21-Feb-2021
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Parallel clustering of high dimensional data by integrating multi-objective genetic algorithm with divide and conquer

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Parallel clustering of high dimensional data by integrating multi-objective genetic algorithm with divide and conquer

Improved k- means clustering algorithm for two dimensional data

A New Data Clustering Approach for Data Mining in Large Databases

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