− Spatial data mining knows a more and more important interest. Fundamental processes of spatial data mining are in particular clustering and structural patterns detection. These processes are influenced strongly by the concept of proximity or neighborhood. This paper introduces some structures to the construction of a spatial data mining integrating fuzzy structural primitives and propose to o...

In this paper, we discuss rough inclusions defined in Rough Mereology – a paradigm for approximate reasoning introduced by Polkowski and Skowron – as a basis for common models for rough as well as fuzzy set theories. We justify the point of view that tolerance (or, similarity) is the motif common to both theories. To this end, we demonstrate in Sect. 6 that rough inclusions (which represent a h...

Obtaining Interpretable Fuzzy Models from Fuzzy Clustering and Fuzzy Regression* Frank Hiippner Frank Klawonn University of Applied Sciences, Emden Department of Electrical Engineering and Computer Science Constantiaplatz 4 D-26723 Emden, Germany e-mail alias: [email protected] In this paper we develop an objective finctionbased clustering algorithm to build fizzy models of the Takagi-Sug...

Background: Marine seaweeds synthesize different types of compounds with various biological activities, including anti-tuberculosis and anticancer effects. Objective: The aim of this study was to evaluate anti tuberculosis of Padina australis and cytotoxicity of different fractions of this seaweed. Methods: Padina australis was collected from coasts of Persian Gulf, identified and e...

This paper deals with the design of a fuzzy PID Controller (FPIDC) with dynamic gain through a fuzzy scheme. The gain factor of Proportional, Integral and Derivative is varied according to process of the proposed dead time. FPIDC is modified which depends on the normalized change of error of the controlled variable (ec) and its number of fuzzy partitions. The proposed scheme is tested for a wid...

The conventional two-stage training algorithm of the fuzzy/neural architecture called FALCON may not provide accurate results for certain type of problems, due to the implicit assumption of independence that this training makes about parameters of the underlying fuzzy inference system. In this correspondence, a training scheme is proposed for this fuzzy/neural architecture, which is based on li...

In this paper we tackle the issue of generating Mamdani fuzzy rule-based systems with optimal trade-offs between complexity and accuracy by using a multi-objective genetic algorithm, which concurrently learns rule base, granularity of the input and output partitions and membership function parameters. To this aim, we exploit a chromosome composed of three parts, which codify, respectively, the ...

In this paper we present a multi-scale method based on the hybrid notion of rough fuzzy sets, coming from the combination of two models of uncertainty like vagueness by handling rough sets and coarseness by handling fuzzy sets. Marrying both notions lead to consider, as instance, approximation of sets by means of similarity relations or fuzzy partitions. The most important features are extracte...

The paper introduces an alternative approach to time-series prediction for stock index data using Interval Type-2 Fuzzy Sets. The work differs from the existing research on time-series prediction by the following counts. First, partitions of the time-series, obtained by fragmenting its valuation space over disjoint equal sized intervals, are represented by Interval Type-2 Fuzzy Sets (or Type-1 ...

Data clustering is a process of putting similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is larger than among groups. This paper reviews four of the most representative off-line clustering techniques: K-means clustering, Fuzzy Cmeans clustering, Mountain clustering, and Subtractive clustering. The techniques are i...