An Advanced Fuzzy C-Means Approach for Effective Big Data Clustering

Abstract

Clustering emerged as powerful mechanism to analyze the massive data generated by modern applications; the main aim of 
it is to categorize the data into clusters where objects are grouped into the particular category. However there are various 
challenges while clustering the big data recently. Deep Learning has been powerful paradigm for big data analysis, this 
requires huge number of samples for training the model, which is time consuming and expensive. This can be avoided though 
fuzzy approach. In this research work, we design and develop an Improvised Fuzzy C-Means (IFCM)which comprises the 
encoder decoder Convolutional Neural Network (CNN) model and Fuzzy C-means(FCM) technique to enhance the clustering 
mechanism. Encoder decoder based CNN is used for learning feature and faster computation. In general FCM, we introduce 
a function which measure the distance between the cluster center and instance which helps in achieving the better clustering 
and later we introduce Optimized Encoder Decoder (OED) CNN model for improvising the performance and for faster 
computation. Further in order to evaluate the proposed mechanism, three distinctive data types namely Modified National 
Institute of Standards and Technology (MNIST), fashion MNIST and United States Postal Service (USPS) are used, also 
evaluation is carried out by considering the performance metric like Accuracy, Adjusted Rand Index (ARI) and Normalized 
Mutual Information(NMI).Moreover,comparativeanalysisiscarriedoutoneachdatasetand 
Comparative analysis shows that IFCM out performs the existing model.