Data Mining and Knowledge Engineering

A. R. Mohamed Shanavas ¹, M. S. Mythili ², D. Regina Margaret ²

Affiliations
1 Department of Computer Science, Jamal Mohamed College, Trichy, IN
2 Department of Computer Applications, Bishop Heber College, Trichy, IN

Abstract

Knowledge and skill of the stake holders are the most dominant factors to evaluate their quality of education. The aim of the paper is to analyze the performance of students of selected UG departments who have consistently figured in the top ten ranks in the first five semester marks by correlating the college marks with their higher secondary marks. The study also includes the correlation of male and female, rural and urban, residential and non residential students. The analysis is done by Statistical Data Mining, Regression Techniques and Hypothesis testing. Data mining is the key to the Knowledge Discovery Database (KDD).. The results of this analysis help the educational institutions to admit quality students for further studies and to retain the good results with high quality education.

Keywords

Data Mining, KDD, DSS, Hypothesis Testing, Prediction.

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A. R. Mohamed Shanavas ¹, M. Arul Kothai Priya ²

Affiliations
1 Jammal Mohamed College, Tiruchirappalli, IN
2 Bharathidasan University, IN

Abstract

The main focus of image mining in the proposed system is concerned with the identification of meningitis in the membrane of brain using CT scan brain image. Identifying the type of tuberculosis affecting the meninges of brain is a crucial step in computer assisted Meningitis TB detection. The system proposes a method based on modified K mean clustering to enhance the diagnosis of medical images like CT scan brain image. The system analyzes medical images and automatically generates suggestions of diagnosis employing modified K mean clustering and Hu Moment Invariant method. The proposed method uses two important algorithm of image mining. The first method extracts features present in the CT scan of brain image and the second method cluster the type of meningitis present in the image. In the existing system it classifies the presence of bacteria through the sputum analysis and identifies the TB affecting the lung. The proposed system identifies Meningitis TB affecting the membranes of the brain. The method has been applied on several real datasets, and the results shows high accuracy to claim that the use of modified K mean clustering is a powerful means to assist in the diagnosing task.

Keywords

Image Mining, Data Mining, Tuberculosis Meningitis (TBM), Hu Moment Invariant, Modified K Mean Clustering.

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