AIRCC's International Journal of Computer Science and Information Technology

Sadiq Hussain ¹, G. C. Hazarika ¹

Affiliations
1 Dibrugarh University, Dibrugarh, Assam, IN

Abstract

Educational Data Mining is a growing trend in case of higher education. The quality of the Educational Institute may be enhanced through discovering hidden knowledge from the student databases/data warehouses. Present paper is designed to carry out a comparative study with the TDC (Three Year Degree) Course students of different colleges affiliated to Dibrugarh University. The study is conducted with major subject wise, gender wise and category/caste wise. The experimental results may be visualized with Scatterplot3D, Bubble Plot, Fit Y by X, Run Chart, Control Chart etc. of the SAS JMP Software.

Keywords

Educational Data Mining, Classification Algorithms, SAS Jmp Software, Knowledge Discovery in Database (KDD), Regression, Bayesian classification.

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Sadiq Hussain ¹, G. C. Hazarika ²

Affiliations
1 Examination Branch, Dibrugarh University, IN
2 Centre for Computer Studies, Dibrugarh University, IN

Abstract

Identifying groups of genes that manifest similar expression patterns is crucial in the analysis of gene expression time series data. Choosing a similarity measure to determine the similarity or distance between profiles is an important task. Time series expression experiments are used to study a wide range of biological systems. More than 80% of all time series expression datasets are short (8 time points or fewer). These datasets present unique challenges. On account of the large number of genes profiled (often tens of thousands) and the small number of time points many patterns are expected to arise at random. Most clustering algorithms are unable to distinguish between real and random patterns. However, the shortness of gene expression time-series data limits the use of conventional statistical models and techniques for time-series analysis. To address this problem, this paper proposes the Fuzzy clustering algorithm based on short time-series, which is able to cluster profiles based on the similarity of their relative change of expression level and the corresponding temporal in- formation. One of the major advantages of fuzzy clustering is that genes can belong to more than one group, revealing distinctive features of each gene's function and regulation.

Keywords

Fuzzy Clustering, Short Time Series, Gene Expression.

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