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Big data clustering using fractional sail fish-sparse fuzzy C-means and particle whale optimization based MapReduce framework

Lecture Notes in Electrical Engineering

Analytical Study of Content-Based and Collaborative Filtering Methods for Recommender Systems

Big data analytics gain significant interest over the traditional data-processing methodologies that engage in extracting the hidden patterns and correlations from the massive data, termed as big data. With the aim of relieving the computational complexity the clustering method plays a significant role. With the knowledge of the clustering algorithms, the big data arriving from the distributed sources is processed using the MapReduce framework (MRF). The MRF possesses two functions, namely, map function and reduce function, such that the map function is based on the proposed Fractional Sparse Fuzzy C-Means (FrSparse FCM) algorithm and reduce function is based on particle swarm optimisation-based whale optimisation algorithm (P-Whale). Initially, the optimal centroids are computed using the proposed algorithm in the mapper phase that is optimally tuned in the reducer phase, and it is clear that the proposed FrSparse FCM-based MRF ensures the parallel processing of the big data. Experimentation is performed using the Skin data set and the localisation data set taken from the UCI machine learning repository, and the analysis is progressed using the metrics, such as accuracy and DB Index. The analysis proves that the proposed method acquired a maximum accuracy of 90.6012% and a minimum DB Index of 5.33.

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IET Image processing jorunal , https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.1049%2Fiet-ipr.2019.0899

Map Reduce Framework based big data clustering using Fractional integrated Sparse Fuzzy C Means algorithm

Multi-label classification is a generalization of a multi-class classification problem where one entity can belong to more than one class from the class set. Recent works have proposed multiple methods of solving this problem that involves both statistical and deep learning methods. While methods exist for using deep learning models for this problem, most of them require the model to have a high dimension output vector and the property of inter-dependency of classes has not been explored. An ensemble of statistical models called the chain classifiers can be used to address these issues. This study explores methods of using neural network classifiers in the classifier chain model and tries to address some problems with such architecture while compare their performance on different types of data using different metrics with each other and with other well performing multi-label classification methods. © 2020 IEEE.

2020 IEEE International Conference for Innovation in Technology, INOCON 2020

Multi-label classification with classifier chains of ANN models

The recent advancements in information technology and the web tend to increase the volume of data used in day-to-day life. The result is a big data era, which has become a key issue in research due to the complexity in the analysis of big data. This paper presents a technique called FPWhale-MRF for big data clustering using the MapReduce framework (MRF), by proposing two clustering algorithms. In FPWhale-MRF, the mapper function estimates the cluster centroids using the Fractional Tangential-Spherical Kernel clustering algorithm, which is developed by integrating the fractional theory into a Tangential-Spherical Kernel clustering approach. The reducer combines the mapper outputs to find the optimal centroids using the proposed Particle-Whale (P-Whale) algorithm, for the clustering. The P-Whale algorithm is proposed by combining Whale Optimization Algorithm with Particle Swarm Optimization, for effective clustering such that its performance is improved. Two datasets, namely localization and skin segmentation datasets, are used for the experimentation and the performance is evaluated regarding two performance evaluation metrics: clustering accuracy and DB-index. The maximum accuracy attained by the proposed FPWhale-MRF technique is 87.91% and 90% for the localization and skin segmentation datasets, respectively, thus proving its effectiveness in big data clustering.

journal of Intelligent System

Fractional Fuzzy Clustering and Particle Whale Optimization-Based MapReduce Framework for Big Data Clustering

Due to ever increasing size of datasets with an extremely high number of attributes, mining of datasets turns out to be a universal scenario in various fields such as, industry, social and scientific areas. In order to search with very large databases, clustering is an important tasks required in various applications. Moreover, clustering algorithms that provide better scaling to very big data are significant and useful. When the size of the data become too big, it is unfeasible to store it in a single computer memory and also it consume more time for entire kernel matrix computation. In order to alleviate this problem, we propose a multiple kernel and a swarm-based MapReduce framework for big data clustering to improve the clustering accuracy. To map the input data, we implement a new clustering algorithm using tangential kernel and spherical kernel, which is used for calculating the relevant cluster centroid. Then, particle swarm based optimization (PSO) is utilized in reducer function, where the fitness value is calculated using Davis-Bouldin (DB) index. The proposed multi kernel swarm based MapReduce framework is experimented with skin and localization data sets. It can be concluded that, the proposed MKSMRF achieves higher clustering accuracy of 81% and 85% for localization and skin dataset respectively, as compared with other clustering algorithms.