poster

MOGCLA: A Multi-Objective Genetic Clustering Algorithm for Large Data Analysis.

Authors:

GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

Pages 1437 - 1438

https://doi.org/10.1145/2739482.2764689

Published: 11 July 2015 Publication History

Get Access

Abstract

Automatic Manifold identification in large datasets is currently a challenging problem in Machine Learning. This process consists on separating a large dataset blindly, according to the form defined by the data instances in the space. Data is discriminated in groups described by their form. These approaches are usually focused on continuity-based methods where the manifold respects a continuity criterion. Currently, Map-Reduce and online clustering techniques try to deal with the discrimination process, but there are a few algorithms which can deal with the manifold extraction process. This work pretends to face this problem from a Genetic-based approach. We have designed a new algorithm, named MOGCLA, which performs a search in two levels combining Map-Reduce and Multi-Objective Optimization. We have compare the new algorithm against other clustering.

References

[1]

C. Fowlkes, S. Belongie, F. Chung, and J. Malik. Spectral grouping using the nystrom method. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 26(2):214--225, 2004.

Digital Library

Google Scholar

[2]

H. D. Menéndez, D. F. Barrero, and D. Camacho. A multi-objective genetic graph-based clustering algorithm with memory optimization. In Evolutionary Computation (CEC), 2013 IEEE Congress on, pages 3174--3181. IEEE, 2013.

Crossref

Google Scholar

[3]

H. D. Menéndez, D. F. Barrero, and D. Camacho. A genetic graph-based aprroach for partitional clustering. International Journal of Neural Systems, 24(03):1430008, 2014.

Crossref

Google Scholar

[4]

T. Murata and H. Ishibuchi. Moga: Multi-objective genetic algorithms. In Evolutionary Computation, 1995., IEEE International Conference on, volume 1, page 289. IEEE, 1995.

Google Scholar

[5]

W. Zhao, H. Ma, and Q. He. Parallel k-means clustering based on mapreduce. In Cloud Computing, pages 674--679. Springer, 2009.

Digital Library

Google Scholar

Cited By

View all

García-Cobo IMenéndez H(2021)Designing large quantum key distribution networks via medoid-based algorithmsFuture Generation Computer Systems10.1016/j.future.2020.09.037115(814-824)Online publication date: Feb-2021
https://doi.org/10.1016/j.future.2020.09.037
Ménendez H(2019)VARMOG: A Co-Evolutionary Algorithm to Identify Manifolds on Large Data2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8790004(3300-3307)Online publication date: 10-Jun-2019
https://dl.acm.org/doi/10.1109/CEC.2019.8790004
Martín AMenéndez HCamacho D(2016)String-based Malware Detection for Android EnvironmentsIntelligent Distributed Computing X10.1007/978-3-319-48829-5_10(99-108)Online publication date: 8-Oct-2016
https://doi.org/10.1007/978-3-319-48829-5_10

Index Terms

MOGCLA: A Multi-Objective Genetic Clustering Algorithm for Large Data Analysis.
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

Recommendations

Combining advantages of new chromosome representation scheme and multi-objective genetic algorithms for better clustering

Various methods have been proposed to utilize Genetic Algorithms (GA) in handling the clustering problem. GA work on encoded strings, namely chromosomes, and the representation of different clusters as a linear structure is an important issue about the ...
Low Dimensional Localized Clustering (LDLC)
ICDM '12: Proceedings of the 2012 IEEE 12th International Conference on Data Mining

In the space of high-dimensional data, it is generally reasonable to assume that the data points are on (or close to) one or more submanifolds. Each of these submanifolds can be modeled by a number of linear subspaces. This is in fact the main intuition ...
Genetic algorithm-based high-dimensional data clustering technique
FSKD'09: Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1

A genetic algorithm-based high-dimensional data clustering technique, called GA-HDclustering, is proposed in this paper. This approach searches feature subspace by genetic algorithms to find the effective clustering feature subspaces. The candidate ...

Comments

Information & Contributors

Information

Published In

GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

July 2015

1568 pages

ISBN:9781450334884

DOI:10.1145/2739482

Editor:
Sara Silva
Universidade de Lisboa, Portugal
,
General Chair:
Anna I. Esparcia-Alcázar
Universitat Politècnica de València, Spain

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 July 2015

Check for updates

Author Tags

Qualifiers

Poster

Funding Sources

Spanish Ministry of Science and Education
Comunidad Autonoma de Madrid
Savier an Airbus Defense and Space

Conference

GECCO '15

Sponsor:

SIGEVO

GECCO '15: Genetic and Evolutionary Computation Conference

July 11 - 15, 2015

Madrid, Spain

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
89
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 21 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

García-Cobo IMenéndez H(2021)Designing large quantum key distribution networks via medoid-based algorithmsFuture Generation Computer Systems10.1016/j.future.2020.09.037115(814-824)Online publication date: Feb-2021
https://doi.org/10.1016/j.future.2020.09.037
Ménendez H(2019)VARMOG: A Co-Evolutionary Algorithm to Identify Manifolds on Large Data2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8790004(3300-3307)Online publication date: 10-Jun-2019
https://dl.acm.org/doi/10.1109/CEC.2019.8790004
Martín AMenéndez HCamacho D(2016)String-based Malware Detection for Android EnvironmentsIntelligent Distributed Computing X10.1007/978-3-319-48829-5_10(99-108)Online publication date: 8-Oct-2016
https://doi.org/10.1007/978-3-319-48829-5_10

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Combining advantages of new chromosome representation scheme and multi-objective genetic algorithms for better clustering

Low Dimensional Localized Clustering (LDLC)

Genetic algorithm-based high-dimensional data clustering technique