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Rough intuitionistic fuzzy C-means algorithm and a comparative analysis

Published: 22 August 2013 Publication History

Abstract

Data clustering algorithms are used in many fields like anonymisation of databases, image processing, analysis of satellite images and medical data analysis. There are several C-Means clustering algorithms in the literature. Besides the hard C-Means, there are uncertainty based C-Means algorithms like the Fuzzy C-Means algorithm and its variants, the Rough C-Means algorithm, the Intuitionistic Fuzzy C- Means algorithm and the hybrid C-Means algorithms (Rough Fuzzy C-Means algorithm). In this paper we propose a new hybrid clustering algorithm called Rough Intuitionistic Fuzzy C-Means and evaluate its performance in comparison to the other algorithms mentioned above. We have applied these algorithms on numerical as well as image data of two different types and used some benchmarking indexes for the evaluation of their performance. The results show that the proposed algorithm outperforms the existing algorithms in almost all cases.

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cover image ACM Other conferences
Compute '13: Proceedings of the 6th ACM India Computing Convention
August 2013
196 pages
ISBN:9781450325455
DOI:10.1145/2522548
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 22 August 2013

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Author Tags

  1. C-means
  2. data clustering
  3. fuzzy C-means
  4. intuitionistic fuzzy C-means
  5. rough C-means
  6. rough fuzzy C-means
  7. rough intuitionistic fuzzy C-means

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Compute '13
Compute '13: The 6th ACM India Computing Convention
August 22 - 25, 2013
Tamil Nadu, Vellore, India

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Compute '13 Paper Acceptance Rate 24 of 96 submissions, 25%;
Overall Acceptance Rate 114 of 622 submissions, 18%

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  • (2023)Impact of new seed and performance criteria in proposed rough k-means clusteringMultimedia Tools and Applications10.1007/s11042-023-14414-082:28(43671-43700)Online publication date: 21-Apr-2023
  • (2022)A Granular Intuitionistic Fuzzy Meta Clustering Algorithm and Its ApplicationProceedings of 2nd International Conference on Artificial Intelligence: Advances and Applications10.1007/978-981-16-6332-1_37(427-442)Online publication date: 14-Feb-2022
  • (2020)Interval Type-2 Fuzzy Local Enhancement Based Rough K-Means Clustering Considering Imbalanced ClustersIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2019.292440228:9(1925-1939)Online publication date: Sep-2020
  • (2020)A new initialization and performance measure for the rough k-means clusteringSoft Computing10.1007/s00500-019-04625-9Online publication date: 2-Jan-2020
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