AN EFFICIENCT RULE MINING MODEL USING [S]3 FCM AND ANN TECHNIQUES

Abstract
We are living in the era known as information era where numerous sectors especially health sectors are put to handle tremendous amount of information. To reduce this burden, an efficient data mining technology has been employed which is a successful evolving technique that has a big future for helping businesses and concentrates on the most valuable data in their data warehouses. Thus this paper presents the review of Safe Semi Supervised Fuzzy C Means (? S?3 FCM) clustering algorithm which is the key aspect of this work aids in achieving the goal. It has been utilized to cluster and classify the clear and relevant global data formats that many other clustering approaches unable to handle. By limiting the subsequent predictions obtained by unsupervised clustering, incorrectly labelled samples are thoroughly investigated. Meanwhile, the other labelled sample’s predictions are equivalent to the assigned labels. As a result, the labelled samples are safely examined using a combination of unsupervised clustering and SSC. Therefore, it has been clear that [S]33 FCM yields better result compared to other techniques. The ANN based classification algorithm is used, which learns from the training dataset to construct a model. This model helps in the classification of new objects.

Authors
T Thamaraiselvan, K Saravanan
Prist University, India

Keywords
Data mining, Clustering, [s]3 FCM, Classification, ANN
Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 12 , Issue: 1 )
Date of Publication :
October 2021
DOI :

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