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Bi-clustering by Multi-objective Evolutionary Algorithm for Multimodal Analytics and Big Data

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Multimodal Analytics for Next-Generation Big Data Technologies and Applications

Abstract

Knowledge discovery is a process of finding hidden knowledge from a large volume of data that involves data mining. Data mining unveils interesting relationships among data and the results can help in making valuable predictions or recommendation in various applications. Bi-clustering is an unsupervised machine learning technique that can uncover useful information from Big data. Bi-clustering has many useful applications in various fields such as pattern classification, information retrieval, gene expression data analysis and functional annotation. The goal of bi-clustering is to detect coherent groups of data by performing clustering along the rows and columns dimension of a dataset simultaneously. Using both the rows and columns information in the data, bi-clustering usually requires the optimization of two or more conflicting objectives. In this chapter, we review some recent state-of-the-art multi-objective, evolutionary-based bi-clustering algorithms and discuss their application in data mining for multimodal and Big data.

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Acknowledgement

Maryam Golchin is supported by the Australian Government Research Training Program Scholarship.

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Authors and Affiliations

  1. School of Information and Communication Technology, Griffith University, Gold Coast, QLD, Australia

    Maryam Golchin & Alan Wee-Chung Liew

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  1. Maryam Golchin
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  2. Alan Wee-Chung Liew
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Corresponding author

Correspondence to Alan Wee-Chung Liew .

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Editors and Affiliations

  1. School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia

    Kah Phooi Seng

  2. School of Information and Communication Technology, Griffith University, Gold Coast, QLD, Australia

    Li-minn Ang

  3. School of Information and Communication Technology, Griffith University, Gold Coast, QLD, Australia

    Alan Wee-Chung Liew

  4. The University of Sydney Business School, University of Sydney, Sydney, NSW, Australia

    Junbin Gao

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Golchin, M., Liew, A.WC. (2019). Bi-clustering by Multi-objective Evolutionary Algorithm for Multimodal Analytics and Big Data. In: Seng, K., Ang, Lm., Liew, AC., Gao, J. (eds) Multimodal Analytics for Next-Generation Big Data Technologies and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-97598-6_6

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  • DOI: https://doi.org/10.1007/978-3-319-97598-6_6

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