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

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.