A new approach for efficient clustering using fuzzy prototypes with varying neighborhoods

DOI

10.2991/978-94-6463-314-6_26How to use a DOI?

Keywords

Prototype; Epsilon; Neighborhood; Fuzzy C-Means; Kernel FCM-F; Kernel FCM-K

Abstract

It is highly desirable to perform the clustering for large datasets more efficiently by finding the approximate clustering results in a reduced time. PFCM, PKFCM-F, and PKFCM-K are recent attempts to improve the efficiency of the traditional FCM, KFCM-F, and KFCM-K algorithms using fuzzy prototypes. Here each prototype represents all the data items in its ϵ neighborhood and the parameter ϵ highly influences the overall performance. Further, it is not possible to determine the optimal value of ϵ beforehand. This article presents a simple and practical approach to finding the ϵ-neighborhood of each prototype on the fly. Empirical results are presented to establish the efficiency of the proposed approach on several publicly available data sets.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - K. Mrudula
AU  - T. Hitendra Sarma
PY  - 2023
DA  - 2023/12/21
TI  - A new approach for efficient clustering using fuzzy prototypes with varying neighborhoods
BT  - Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023)
PB  - Atlantis Press
SP  - 258
EP  - 265
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-314-6_26
DO  - 10.2991/978-94-6463-314-6_26
ID  - Mrudula2023
ER  -