Data mining can be said to have the aim to analyze the observational datasets to find relationships and to present the data in ways that are both understandable and useful. In this thesis, some existing intelligent systems techniques such as Self-Organizing Map, Fuzzy C-means and decision tree are used to analyze several datasets. The techniques are used to provide flexible information processi...

introduction: virtual bronchoscopy is a reliable and efficient diagnostic method for primary symptoms of lung cancer. the segmentation of airways from ct images is a critical step for numerous virtual bronchoscopy applications. materials and methods: to overcome the limitations of the fuzzy connectedness method, the proposed technique, called fuzzy connectivity - fuzzy c-mean (fc-fcm), utilized...

Knowledge systems are characterized by being active, uncertain and adaptive in pervasive computing environments, and Fuzzy Event-Condition-Action (ECA) rules can effectively describe these features of knowledge systems. To analyze the static structure and dynamic behavior of fuzzy ECA rules, a fuzzy colored time Petri net model (FCTPN) is proposed. The FCTPN is able to model the fuzzy ECA rules...

A significant data mining issue is the effective discovery of association rules. The extraction of association rules faces the problem of the combinatorial explosion of the search space, and the loss of information by the discretization of values. The first problem is confronted effectively by the Frequent Pattern Tree approach of [10]. This approach avoids the candidate generation phase of Apr...

In this paper, we propose a unitary tool for modeling and analysis of discrete event systems monitoring. Uncertain knowledge of such tasks asks specific reasoning and adapted fuzzy logic modeling and analysis methods. In this context, we propose a new fuzzy Petri net called Fuzzy Reasoning Petri Net: the FRPN. The modeling consists in a set of two collaborative FRPN. The first is used for the f...