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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

Cluster Analysis Using Firefly-Based K-means Algorithm: A Combined Approach

verfasst von : Janmenjoy Nayak, Bighnaraj Naik, H. S. Behera

Erschienen in: Computational Intelligence in Data Mining

Verlag: Springer Singapore

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Abstract

Nature-inspired algorithms have evolved as a hot topic of research interest around the globe. Since the last decade, K-means clustering has become an attractive area for researchers towards solving many real-world clustering problems. But, unfortunately K-means does not work well for non-globular clusters. Firefly algorithm is a recently developed metaheuristic algorithm that simulates through the flashing characteristics of the fireflies. The firefly algorithm uses the capacity of global search to resolve the limitations of K-means technique and helps in escaping from the local optima. In this work, a novel firefly-based K-means algorithm (FA-K-means) has been proposed for efficient cluster analysis and the results of the proposed approach are compared with some other benchmark approaches. Simulation results divulge that the proposed approach can be efficiently used for solving clustering problems as it avoids the trapping in local optima and helpful for faster convergence.

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Vorheriges Kapitel Cost Effectiveness Analysis of a Vertical Midimew-Connected Mesh Network (VMMN)
Nächstes Kapitel A Study of Dimensionality Reduction Techniques with Machine Learning Methods for Credit Risk Prediction
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Metadaten
Titel
Cluster Analysis Using Firefly-Based K-means Algorithm: A Combined Approach
verfasst von
Janmenjoy Nayak
Bighnaraj Naik
H. S. Behera
Copyright-Jahr
2017
Verlag
Springer Singapore
Buch
Computational Intelligence in Data Mining

Print ISBN: 978-981-10-3873-0

Electronic ISBN: 978-981-10-3874-7

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-981-10-3874-7_6