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Fuzzy Model Based Predictive Control of Reaction Temperature in a Pilot Plant Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Ant Colony Based Fuzzy C-Means Clustering for Very Large Data

Authors : Dhruv Mullick, Ayush Garg, Arpit Bajaj, Ayush Garg, Swati Aggarwal

Published in: Advances in Fuzzy Logic and Technology 2017

Publisher: Springer International Publishing

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Abstract

Fuzzy C-Means (FCM) is a popular technique for clustering of data. It combines the concepts of K-Means algorithm and Fuzzy set theory. However, FCM faces the challenges of running into a local optimal value, and of producing results which are sensitive to initialisation conditions. To solve these problems, there has been prior work which incorporates Ant Colony Optimisation (ACO) into the conventional FCM algorithm. The authors of this paper find that though the FCM-ACO algorithm is a definite improvement over the traditional FCM, there is still scope for improving the scalability and accuracy of the system. The authors propose using a Multi Round Sampling (MRS) technique along with Ant colony Optimisation. The proposed algorithm allows us to cluster the dataset without considering it entirely, hence allowing for a more space and time efficient system. This makes the system highly scalable and hence suitable for large datasets. Moreover, extensive experiments on several publicly available datasets, both large and small, prove that the proposed algorithm of Multi Round Sampling of Ant Colony based Fuzzy C-Means (MRSA-FCM) gives superior clustering results, over the FCM and FCM-ACO systems.

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Footnotes
1
2D15 clustering dataset can be found at [1].
 
2
MNIST clustering dataset can be found at [3].
 
3
Forest clustering dataset can be found at [2].
 
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Metadata
Title
Ant Colony Based Fuzzy C-Means Clustering for Very Large Data
Authors
Dhruv Mullick
Ayush Garg
Arpit Bajaj
Ayush Garg
Swati Aggarwal
Copyright Year
2018
Book
Advances in Fuzzy Logic and Technology 2017

Print ISBN: 978-3-319-66823-9

Electronic ISBN: 978-3-319-66824-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-66824-6_51

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