This paper concerns some aspects of rough set based data analysis. In particular rough set look on Bayes’ formula leads to new methodology of reasoning from data and shows interesting relationship between Bayes’ theorem, rough sets and flow graphs. Three methods of flow graphs application in drawing conclusions from data are presented and examined. MOTTO: “It is a capital mistake to theorise be...

Article history: Received 18 September 2012 Received in revised form 13 July 2013 Accepted 1 September 2013 Available online 20 September 2013

In this article, we present some extensions of the rough set approach and we outline a challenge for the rough set based research. 2006 Elsevier Inc. All rights reserved.

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The articles [2], [3], [1], [4], and [5] provide the terminology and notation for this paper. For simplicity, we adopt the following rules: G denotes a group, A, B denote non empty subsets of G, N , H, H1, H2 denote subgroups of G, and x, a, b denote elements of G. Next we state a number of propositions: (1) For every normal subgroup N of G and for all elements x1, x2 of G holds x1 ·N · (x2 ·N)...

Article history: Available online 23 March 2013

We prove that the operators in a class of rough fractional integral operators and the related maximal operators are bounded from L p (v p) to L q (u q) with weight pair (u,v).