Data Mining : Concept and Techniques

TL;DR: This paper presents an overview of research directions for applying supervised and unsupervised methods for managing the problem of anomaly detection, and covers the major theoretical issues.

TL;DR: Important issues concerning the collection of data on the social dynamics of almost entire populations of individuals, and their processing and analysis, are reviewed to identify the most promising approaches in the emerging field of 'reality mining'.

TL;DR: A feature selection-cum-classification scheme has been devised by combining F-statistic, statistical learning techniques i.e., Bayesian learning and support vector machine (SVM) in order to provide the higher classification accuracy using best set of discriminating features.

TL;DR: In this paper, the authors systematically review the relevant articles published between 2013 and 2018, focusing on promising directions that can dominate future research in this field and further extend the history of several interacting fields, including big data and economic theories, toward methodological rather than application dimensions.