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Uma Rani, R.
- A Novel Clustering Based Feature Selection for Classifying Student Performance
Authors
1 Department of Computer Science, SSM College of Arts & Science, PinCode-638 183, Tamilnadu, IN
2 Department of Computer Science, Sri Sarada College for Women, PinCode-638 016, Tamilnadu, IN
Source
Indian Journal of Science and Technology, Vol 8, No S7 (2015), Pagination: 135-140Abstract
The main intent of this work is to identify and eradicate the irrelevant as well as redundant features that are used to improve the accuracy of student performance classification.In this article, a novel technique is introduced for feature or attributes selection purpose called as Non-negative Matrix Factorization Clustering based Feature Selection (NMFCFS). NMFCFS uses symmetric uncertainty (SU) estimation.
The performance of this work is evaluated by using the student dataset that includes collection of students' information from various colleges. For analyzing the performance of this work, comparative evaluation is performed between the classifiers (in the experiment, Prism and J48 is taken) without feature selection and classifiers with the NMFCFS. The experimental result shows that NMFCFS approach is attaining higher accuracy rate i.e., 97.8%. The proposed feature selection method is highly efficient when compared to other schemes.
The findings demonstrate that the proposed method has high performance of the students' failure and dropout prediction. In other words, this can improve the accuracy of the classification result.
Keywords
Educational Data Mining (EDM), Feature Selection, Non-Negative Matrix Factorization, Symmetric Uncertainty (SU) and Classification.Full Text
- Management of Stress Among Employees in BPO Using Clustering Algorithm
Authors
1 Periyar University, IN
2 Sri Saradha College for Women, Salem, IN
Source
Data Mining and Knowledge Engineering, Vol 4, No 7 (2012), Pagination: 349-351Abstract
Business process outsourcing, the most flourishing Indian industry sector has emerged as India's most promising sector, and has been growing at a rate of 40-50 per cent since its inception. BPO is a very fast paced and a high momentum industry. BPO industry is expected to generate two million jobs by the year 2012. Taking advantage form the abundant skills and low cost benefits, large numbers of BPO companies have mushroomed in India in recent years, many of the well established IT companies have also started their BPO divisions. But, there is another side of the BPO picture too. The side that has already brought the BPO industry in limelight many times. This study is concerned with the non viability of the BPO and the fact that the young generation of India is actually losing out in the BPO.In statistics and data mining, k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. This results into a partitioning of the data space into Voronoi cells.
This paper analysis the BPO employees working culture. We use k-means clustering algorithm for analysis the selected dataset.
