Golchin, Maryam, and Liew, Alan Wee-Chung (2019) Bi-clustering by Multi-objective Evolutionary Algorithm for Multimodal Analytics and Big Data. In: Seng, Kah Phooi, Ang, Li-minn, Liew, Alan Wee-Chung, and Gao, Junbin, (eds.) Multimodal Analytics for Next-Generation Big Data Technologies and Applications. Springer, Cham, Switzerland, pp. 125-150.

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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.

Item ID:	76977
Item Type:	Book Chapter (Scholarly Work)
ISBN:	978-3-319-97598-6
Copyright Information:	© Springer Nature Switzerland AG 2019
Date Deposited:	08 Dec 2022 00:40
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4613 Theory of computation > 461305 Data structures and algorithms @ 100%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences @ 100%
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