Outlier Detection Method on UCI Repository Dataset by Entropy Based Rough K-means

Rough set theory is used to handle uncertainty and incomplete information by applying two sets, lower and upper approximation. In this paper, the clustering process is improved by adapting the preliminary centroid selection method on rough K-means (RKM) algorithm. The entropy based rough K-means (ERKM) method is developed by adapting entropy based preliminary centroids selection on RKM and executed and also validated by cluster validity indexes. An example shows that the ERKM performs effectively by selection of entropy based preliminary centroid. In addition, Outlier detection is an important task in data mining and very much different from the rest of the objects in the cluster. Entropy based rough outlier factor (EROF) method is used to detect outlier effectively for yeast dataset. An example shows that EROF detects outlier effectively on protein localisation sites and ERKM clustering algorithm performed effectively. Further, experimental readings show that the ERKM and EROF method outperformed the other methods.

Perceived trends in viewing the future by Croatian and Slovenian business students: Implications for managerial education

This paper presents appreciative inquiry (AI) methodology in the context of management education. Therefore, we propose that AI, which focuses on positive aspects of doing business, needs to be implemented into management education of future managers in Slovenia and Croatia and we provide empirical evidence of thinking patterns of business students studying at the University of Ljubljana and the University of Split. For comparison, we investigated the previous experiences and future expectations of business students in Croatia and Slovenia. Empirical part is composed of two parts: qualitatively based AI and a quantitative study, based on statistical methods. Results show a positive outlook of business students in both countries