Sanjay Chakraborty

Work place: Department of Computer Science & Engineering, Institute of Engineering & Management, Kolkata, India



Research Interests: Network Architecture, Network Security, Data Mining, Data Structures and Algorithms, Quantum Computing Theory


Sanjay Chakraborty: He has completed his B-Tech from West Bengal University of Technology, India on Information Technology in the year 2009. He has completed his Master of Technology from National Institute of Technology, Raipur, India in the year of 2011. Now, He is working as an Assistant Professor at Department of Computer Science & Engineering in Institute of Engineering & Management, Kolkata. His areas of interests are Data Mining, Cryptography & Network Security, Cloud computing and Quantum Computing. He is a professional member of IAENG and UACEE. He has published several research papers in various reputed journals and international conferences.

Author Articles
An Univariate Feature Elimination Strategy for Clustering Based on Metafeatures

By Saptarsi Goswami Sanjay Chakraborty Himadri Nath Saha

DOI:, Pub. Date: 8 Oct. 2017

Feature selection plays a very important role in all pattern recognition tasks. It has several benefits in terms of reduced data collection effort, better interpretability of the models and reduced model building and execution time. A lot of problems in feature selection have been shown to be NP – Hard. There has been significant research in feature selection in last three decades. However, the problem of feature selection for clustering is still quite an open area. The main reason is unavailability of target variable as compared to supervised tasks. In this paper, five properties or metafeatures like entropy, skewness, kurtosis, coefficient of variation and average correlation of the features have been studied and analysed. An extensive study has been conducted over 21 publicly available datasets, to evaluate viability of feature elimination strategy based on the values of the metafeatures for feature selection in clustering. A strategy to select the most appropriate metafeatures for a particular dataset has also been outlined. The results indicate that the performance decrease is not statistically significant.

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Sentiment Analysis of Review Datasets Using Naïve Bayes‘ and K-NN Classifier

By Lopamudra Dey Sanjay Chakraborty Anuraag Biswas Beepa Bose Sweta Tiwari

DOI:, Pub. Date: 8 Jul. 2016

The advent of Web 2.0 has led to an increase in the amount of sentimental content available in the Web. Such content is often found in social media web sites in the form of movie or product reviews, user comments, testimonials, messages in discussion forums etc. Timely discovery of the sentimental or opinionated web content has a number of advantages, the most important of all being monetization. Understanding of the sentiments of human masses towards different entities and products enables better services for contextual advertisements, recommendation systems and analysis of market trends. The focus of our project is sentiment focussed web crawling framework to facilitate the quick discovery of sentimental contents of movie reviews and hotel reviews and analysis of the same. We use statistical methods to capture elements of subjective style and the sentence polarity. The paper elaborately discusses two supervised machine learning algorithms: K-Nearest Neighbour(K-NN) and Naïve Bayes‘ and compares their overall accuracy, precisions as well as recall values. It was seen that in case of movie reviews Naïve Bayes‘ gave far better results than K-NN but for hotel reviews these algorithms gave lesser, almost same accuracies.

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