Outlier Detection: A Research and Modified Method Using Fuzzy Clustering
S. Rajalakshmi1, P. Madhubala2

1S. Rajalakshmi, Department of Computer Science, Government Arts College for women, Krishnagiri (Tamil Nadu), India.

2Dr. P. Madhubala, HOD, Department of Computer Science, DonBosco College, Dharmapuri (Tamil Nadu), India.

Manuscript received on 12 January 2020 | Revised Manuscript received on 08 February 2020 | Manuscript Published on 20 February 2020 | PP: 427-431 | Volume-9 Issue-3S January 2020 | Retrieval Number: C10910193S20/2020©BEIESP | DOI: 10.35940/ijitee.C1091.0193S20

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Data mining is becoming increasingly popular in many application fields. Due to the advancement, Researchers show great interest to find unexpected behaviour over large amount of datasets. Outlier detection is studied extensively in data mining and developed for certain application domains, while others are generic in nature. It is one of the important and hottest topic in research which faces a series of new challenges. It occurs due to change in system behaviour, mechanical fault, human error, natural deviations and instrumental error. The purpose of this paper briefly provides a survey on outlier detection and a modified approach to detect outlier using Fuzzy clustering. Also, it provides a better understanding of different dimensions that applied in various substantive areas.

Keywords: Data Mining, Fuzzy Clustering, Outlier Detection.
Scope of the Article: Fuzzy Logics