scispace - formally typeset
Search or ask a question

Showing papers by "Zdzisław Pawlak published in 2005"


Journal ArticleDOI
TL;DR: This paper outlines a new approach to conflict analysis, which will be illustrated by a simple tutorial example of voting analysis in conflict situations.

163 citations


Book ChapterDOI
TL;DR: It is revealed that flow in a flow graph is governed by Bayes’ rule, but the rule has an entirely deterministic interpretation without referring to its probabilistic roots.
Abstract: In this paper we propose a new approach to data mining and knowledge discovery based on information flow distribution in a flow graph. Flow graphs introduced in this paper are different from those proposed by Ford and Fulkerson for optimal flow analysis and they model flow distribution in a network rather than the optimal flow which is used for information flow examination in decision algorithms. It is revealed that flow in a flow graph is governed by Bayes’ rule, but the rule has an entirely deterministic interpretation without referring to its probabilistic roots. Besides, a decision algorithm induced by a flow graph and dependency between conditions and decisions of decision rules is introduced and studied, which is used next to simplify decision algorithms.

58 citations


Book ChapterDOI
31 Aug 2005
TL;DR: It is shown that flow graph can be used both as formal language for computing approximations of sets in the sense of rough set theory, and as description tool for data structure, to this end decision algorithm induced by the flow graph is defined and studied.
Abstract: This paper concerns the relationship between rough sets and flow graphs. It is shown that flow graph can be used both as formal language for computing approximations of sets in the sense of rough set theory, and as description tool for data structure. This description is employed next for finding patterns in data. To this end decision algorithm induced by the flow graph is defined and studied.

43 citations


Book ChapterDOI
TL;DR: This article is intended to present some philosophical observations rather than to consider technical details or applications of rough set theory, and refrain from presentation of many interesting applications and some generalizations of the theory.
Abstract: This article presents some general remarks on rough sets and their place in general picture of research on vagueness and uncertainty – concepts of utmost interest, for many years, for philosophers, mathematicians, logicians and recently also for computer scientists and engineers particularly those working in such areas as AI, computational intelligence, intelligent systems, cognitive science, data mining and machine learning. Thus this article is intended to present some philosophical observations rather than to consider technical details or applications of rough set theory. Therefore we also refrain from presentation of many interesting applications and some generalizations of the theory.

43 citations


Book ChapterDOI
01 Jan 2005
TL;DR: The introduced flow graphs differ from that proposed by Ford and Fulkerson, for they do not describe material flow in the flow graph but information “flow” about the data structure.
Abstract: This paper concerns a new approach to data analysis based on information flow distribution study in flow graphs. The introduced flow graphs differ from that proposed by Ford and Fulkerson, for they do not describe material flow in the flow graph but information “flow” about the data structure.

5 citations


Journal Article
TL;DR: Fuzzy set and rough set theory are two different approaches to vagueness and are not remedy for classical set theory difficulties, but are rather complementary.
Abstract: We outline the relationship between classical (orthodox) sets from one side, and fuzzy and rough (non-orthodox) sets from another side. The classical concept of a set used in mathematics leads to antinomies, i.e., it is contradictory. This deficiency has, however, rather philosophical than practical meaning. Antinomies are associated with very “artificial” sets constructed in logic but not found in sets used in mathematics. That is why one can use mathematics safely. Fuzzy set and rough set theory are two different approaches to vagueness and are not remedy for classical set theory difficulties. Fuzzy set theory addresses gradualness of knowledge, expressed by the fuzzy membership, whereas rough set theory addresses granularity of knowledge, expressed by the indiscernibility relation. From practical point of view both theories are not competing but are rather complementary.

2 citations