Data Mining and Knowledge Engineering

K. Fathima Bibi ¹, M. Nazreen Banu ²

Affiliations
1 Dept. of Comp. Sc., Bharathiyar University, Coimbatore, TN, IN
2 Dept. of Comp. Sc. & Engg., MAM College of Engineering, Tiruchirappalli, TN, IN

Abstract

Feature Selection plays an important role in data mining. Dealing with excessive number of features has become a computational burden on learning algorithms. Removing irrelevant and redundant features makes data mining task more efficient and improves its accuracy. In this review, different feature selection approaches, relation between them and the various learning algorithms are discussed. Applications that support the use of feature selection technique are also included. We conclude this work by reviewing the contribution of the various feature selection approaches.

Keywords

Feature Selection, Classification, Clustering, Supervised, Unsupervised, Semi-Supervised.

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J. Hajiram Beevi ¹, M. Nazreen Banu ²

Affiliations
1 Department of Computer Science and Information Technology, Jamal Mohamed College, Trichy, Tamilnadu, IN
2 Department of Computer Science and Engineering, M.A.M. College of Engineering, Trichy, Tamilnadu, IN

Abstract

This paper makes a broad survey on the various web 2.0 tools that are used to create learning resources in E-learning 2.0 environments. Interestingly, these tools provide the capacity for supporting modern and independent lifelong learners and they can be applicable for all the stages of e-learning system development. They shift the roles of a teacher into a mentor and also make the learners as active participants. This paper shows the influential contribution of Web 2.0 tools and their applications on higher education. Also, it provides some real time examples for creating learning resources using Web 2.0 tools. Further, it proves that Web 2.0 tools have a strong positive impact on e-learning which shapes the students' social attitudes. In addition, it presents the characteristics of various web 2.0 tools and analyzes each of them separately.

Keywords

Web 1.0, Web 2.0, E-Learning 1.0, RSS, E-Learning 2.0, Podcasting.

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