Chaotic Tornadogenesis Optimization Algorithm for Data Clustering Problems
This article describes how clustering is an attractive and major task in data mining in which particular set of objects are grouped according to their similarities based on some criteria. Among the numerous algorithms, k-Means is the best and efficient in address clustering problems. Any expert system is said to be good, only if it returns the optimal data clusters. The challenge of optimal clustering lies in finding the optimal number of clusters and identifying all the data groups correctly which is a NP-hard problem. Recently a new optimization algorithm TOA was developed to address these problems. However, the standard TOA is too often trapped at the local optima and premature convergence. To overcome this, this article proposes CTOA. The main objective of embedding chaotic maps into standard TOA is to compute and automatically adapt the internal parameters. The proposed CTOA is first benchmarked on standard mathematical functions and later applied to 10 data clustering problems. The obtained graphical and statistical results along with comparisons illustrate the capabilities of CTOA regarding accuracy and robustness
Year of publication: |
2018
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Authors: | Saidala, Ravi Kumar ; Devarakonda, Nagaraju |
Published in: |
International Journal of Software Science and Computational Intelligence (IJSSCI). - IGI Global, ISSN 1942-9037, ZDB-ID 2703774-5. - Vol. 10.2018, 1 (01.01.), p. 38-64
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Publisher: |
IGI Global |
Subject: | Algorithms | Chaotic Maps | Cognitive Algorithms | Data Clustering Problem | Nature Inspired Meta heuristic optimization | TOA |
Saved in:
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