Informatics Publishing Limited

R. Sumithra ¹, Sujni Paul ², V. Thavavel ³

Affiliations
1 School of Computer Science, CMS College, Coimbatore, IN
2 Department of MCA, Oxford College of Engineering, Bangalore, IN
3 Department of Computer Applications, Karunya University, Coimbatore, IN

Abstract

This paper presents a review of how data mining is used in cloud computing as discussed in various research contributions. Cloud computing has become a viable mainstream solution for data processing, storage and distribution. It promises on demand, scalable, pay-as-you-go compute and storage capacity. To analyze "big data" on clouds, it is very important to research data mining strategies based on cloud computing paradigm from both theoretical and practical views. The purpose of this paper is to study a strategy of data mining on cloud. Hadoop, one of the open source implementation of Map reduce framework is very useful in distributed data mining concepts. While outsourcing data to the mining process in cloud environment the integrity of data should be maintained. Here a discussion is being given on cloud computing security issues and solutions as given in various research papers.

Keywords

Cloud Computing, Association Rule Mining, Map Reduce, Data Integrity, Outsourcing, Security Model.

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T. R. Sivapriya ¹, A. R. Nadira Banu Kamal ², V. Thavavel ³

Affiliations
1 Department of Computer Science, Lady Doak College, Madurai, Tamil Nadu, IN
2 Department of MCA and Department of Computer Science, TBAK College for Women, Kilakarai, Ramnad District, Tamil Nadu, IN
3 Department of MCA, Karunya University, Coimbatore, Tamilnadu, IN

Abstract

Feature selection is of great importance in medical image classification especially neuroimaging classification for determining the most relevant features that will aid in accurate diagnosis of neuropsychological diseases. This paper presents a comparison of feature selection algorithms based on Support Vector Machine (SVM). To achieve robust performance and optimal selection of parameters involved in feature selection, and classification, prior knowledge is embedded to generate multiple versions of training and testing sets for parameter optimization. The integrated feature extraction and selection method is applied to a Structural Magnetic Resonance image based Alzheimer's dementia (AD) study with four different sets of non-demented and demented subjects. Cross-validation results of our study clearly indicate that the algorithm SVM-RFE trained with prior knowledge achieves 98% accuracy with Radial Basis Function (RBF) kernel and can improve performance of the classifier. This novel method of inculcating prior knowledge in SVM-RFE method which is tested in 4 different sets of datasets reveals that RBF kernel is found to outperform other kernels with a mean sensitivity of 97%, and thereby aids in quick and efficient classification of dementia.

Keywords

Support Vector Machine, Classification, Dementia, SVM-RFE.

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