An Optimized Three-Dimensional Clustering for Microarray Data

An Optimized Three-Dimensional Clustering for Microarray Data

Narmadha N. (Periyar University, India) and R. Rathipriya (Periyar University, India)
Copyright: © 2020 |Pages: 12
DOI: 10.4018/978-1-7998-0106-1.ch017

Abstract

This chapter focuses on discrete firefly optimization algorithm (FA)-based microarray data, which is a meta-heuristic, bio-inspired, optimization algorithm based on the flashing behaviour of fireflies, or lighting bugs. Its primary advantage is the global communication among the fireflies, and as a result, it seems more effective for triclustering problem. This chapter aims to render a clear description of a new firefly algorithm (FA) for optimization of tricluster applications. This research work proposes discrete firefly optimization-based triclustering model first time to find the highly correlated tricluster from microarray data. This model is reliable and robust triclustering model because of efficient global communication among the swarming particles called fireflies.
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1.1 Introduction

This chapter focuses on Discrete Firefly Optimization Algorithm (FA) based Microarray Data which is a meta-heuristic, bio-inspired, optimization algorithm based on the flashing behaviour of fireflies, or lighting bugs. Its primary advantage is the global communication among the fireflies, and as a result, it seems more effective for triclustering problem.

This chapter aims to render a clear description of a new Firefly Algorithm (FA) for optimization of tricluster applications. The chapter organization is given as follows. Section 2 discusses the Firefly Optimization Algorithm. Section 3 outlines the development of the proposed biclustering model. Hybrid Firefly Optimization Based Biclustering model is introduced and discussed in the Section 4. The experimental results analysis of the proposed models is elaborated in the Section 5. Section 6 concludes the chapter with the future research possibility.

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