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Inter-class and Intra-class Fuzzy Clustering with Pruning Algorithm

A. B. Kulkarni1 , S.V. Bonde2 , U.V. Kulkarni3

  1. CSE Department, SGGSIE&T, SRTM University, Nanded, India.
  2. EXTC Department, SGGSIE&T, SRTM University, Nanded, India.
  3. CSE Department, SGGSIE&T, SRTM University, Nanded, India.

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-5 , Page no. 94-99, May-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i5.9499

Online published on May 31, 2018

Copyright © A. B. Kulkarni, S.V. Bonde, U.V. Kulkarni . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: A. B. Kulkarni, S.V. Bonde, U.V. Kulkarni, “Inter-class and Intra-class Fuzzy Clustering with Pruning Algorithm,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.94-99, 2018.

MLA Style Citation: A. B. Kulkarni, S.V. Bonde, U.V. Kulkarni "Inter-class and Intra-class Fuzzy Clustering with Pruning Algorithm." International Journal of Computer Sciences and Engineering 6.5 (2018): 94-99.

APA Style Citation: A. B. Kulkarni, S.V. Bonde, U.V. Kulkarni, (2018). Inter-class and Intra-class Fuzzy Clustering with Pruning Algorithm. International Journal of Computer Sciences and Engineering, 6(5), 94-99.

BibTex Style Citation:
@article{Kulkarni_2018,
author = {A. B. Kulkarni, S.V. Bonde, U.V. Kulkarni},
title = {Inter-class and Intra-class Fuzzy Clustering with Pruning Algorithm},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2018},
volume = {6},
Issue = {5},
month = {5},
year = {2018},
issn = {2347-2693},
pages = {94-99},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1943},
doi = {https://doi.org/10.26438/ijcse/v6i5.9499}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i5.9499}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1943
TI - Inter-class and Intra-class Fuzzy Clustering with Pruning Algorithm
T2 - International Journal of Computer Sciences and Engineering
AU - A. B. Kulkarni, S.V. Bonde, U.V. Kulkarni
PY - 2018
DA - 2018/05/31
PB - IJCSE, Indore, INDIA
SP - 94-99
IS - 5
VL - 6
SN - 2347-2693
ER -

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Abstract

The paper proposes a new supervised fuzzy clustering algorithm based on inter-class and intra-class clustering technique to create the clusters i.e. fuzzy hyperspheres (FHSs) and pruning technique to prune redundant FHSs which are camouflaged by the other FHSs of the same class. The proposed clustering technique finds the centroid and the width of the FHS based on the spread of inter-class patterns and then groups intra-class patterns using fuzzy membership function, whereas the pruning technique creates the optimal number of FHSs from the FHSs created in the earlier stage. This algorithm is independent of parameters, limits the interference of outliers and converges quickly to create an optimal number of clusters. The main feature of the proposed fuzzy clustering algorithm is that it camouflages the clustered patterns giving 100% accuracy for any training dataset. The performance of the proposed algorithm is tested on eleven benchmark datasets and it is observed that the proposed algorithm results are superior and comparable with classifiers using clustering algorithm.

Key-Words / Index Term

Fuzzy clustering, Fuzzy membership function, Fuzzy hyperspheres, pruning

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