Data Mining and Knowledge Engineering

V. Maria Antoniate Martin ¹, K. David ², R. Bhuvaneswari ³

Affiliations
1 Department of Computer Science, Research and Development Centre, Bharathiar University, Coimbatore-641046, Tamil Nadu, IN
2 Department of Computer Science, H.H. The Rajah's College, Pudukkottai-622001, Tamil Nadu, IN
3 Department of Computer Science, St. Joseph's College, Trichy-620002, IN

Abstract

Sentiment analysis could be a range of language process for chase the mood of the general public a few unambiguous product or topic. Sentiment analysis, which is additionally called as opinion mining, involves in building a system to gather and examine opinions regarding the merchandise created in journal posts, comments, reviews or tweets. Here in this paper various sentiment analysis techniques are reviewed. The review sites mentioned for the awareness and various author's proposals are tabulated.

Keywords

Sentiment Analysis, Opinion Mining, Joint Segmentation, NLP, POS Tagging.

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R. Mangai Begum , K. David

Abstract

In recent years, the data mining techniques have met a serious challenge due to an increased concerned and worries of the privacy that is protecting the privacy of the critical and sensitive data. Different techniques and algorithms have been already presented for PPDM which could be classified in three common approaches. 1) Anonymization approach 2) Randomization approach 3) Cryptographic approach. This paper provides on the types of attacks and attack based classification of privacy preservation data mining, as a foundation for further research in this field.

Keywords

PPDM, Anonymization, Randomization, Cryptographic.

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R. Mangai Begum ¹, K. David ²

Affiliations
1 Bharathiar University, Coimbatore, IN
2 Department of CSE, Roever Engg. College, Perambalur-621212, IN

Abstract

Educational Data Mining (EDM) is a recent trend where data mining methods are experiments for the improvement of student performance in academics. This work describes the method of measuring the students learning behavior based on PTT (personality trait test). An experiment is attempted to improve the accuracy for predicting which student's Personality Trait uses all the available attributes. The student behavior was measured into five personality trait variables using PTQ (Personality trait Questionnaire). These variables are extroversion, agreeableness, consciousness, neuroticism, openness to experience. The data mining technique of attribute selection is applied for selecting the best attributes. Attribute selection is done by using WEKA tool. Data is rebalanced using cost responsive classification. The classification algorithms naive bayes, J48, Random forest, AD3, and Random tree are grouped with analysis and was experimented using Weka tool. The resultant analysis is compared with learning behavior of the student data based on the dataset PTQ and Classification methods.

Keywords

Classification, Educational Data Mining (EDM), Personality Test, Weka Tool.

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