Displaying similar documents to “On the comparison of some fuzzy clustering methods for privacy preserving data mining: Towards the development of specific information loss measures”

Neuro-rough-fuzzy approach for regression modelling from missing data

Krzysztof Simiński (2012)

International Journal of Applied Mathematics and Computer Science

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Real life data sets often suffer from missing data. The neuro-rough-fuzzy systems proposed hitherto often cannot handle such situations. The paper presents a neuro-fuzzy system for data sets with missing values. The proposed solution is a complete neuro-fuzzy system. The system creates a rough fuzzy model from presented data (both full and with missing values) and is able to elaborate the answer for full and missing data examples. The paper also describes the dedicated clustering algorithm....

Object-parameter approaches to predicting unknown data in an incomplete fuzzy soft set

Yaya Liu, Keyun Qin, Chang Rao, Mahamuda Alhaji Mahamadu (2017)

International Journal of Applied Mathematics and Computer Science

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The research on incomplete fuzzy soft sets is an integral part of the research on fuzzy soft sets and has been initiated recently. In this work, we first point out that an existing approach to predicting unknown data in an incomplete fuzzy soft set suffers from some limitations and then we propose an improved method. The hidden information between both objects and parameters revealed in our approach is more comprehensive. Furthermore, based on the similarity measures of fuzzy sets, a...

An ε-insensitive approach to fuzzy clustering

Jacek Łęski (2001)

International Journal of Applied Mathematics and Computer Science

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Fuzzy clustering can be helpful in finding natural vague boundaries in data. The fuzzy c-means method is one of the most popular clustering methods based on minimization of a criterion function. However, one of the greatest disadvantages of this method is its sensitivity to the presence of noise and outliers in the data. The present paper introduces a new ε-insensitive Fuzzy C-Means (εFCM) clustering algorithm. As a special case, this algorithm includes the well-known Fuzzy C-Medians...

Fuzzy clustering: Insights and new approach.

Frank Klawonn (2004)

Mathware and Soft Computing

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Fuzzy clustering extends crisp clustering in the sense that objects can belong to various clusters with different membership degrees at the same time, whereas crisp or deterministic clustering assigns each object to a unique cluster. The standard approach to fuzzy clustering introduces the so-called fuzzifier which controls how much clusters may overlap. In this paper we illustrate, how this fuzzifier can help to reduce the number of undesired local minima of the objective function that...

Learning imprecise semantic concepts from image databases.

Daniel Sánchez, Jesús Chamorro-Martínez (2002)

Mathware and Soft Computing

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In this paper we introduce a model to represent high-level semantic concepts that can be perceived in images. The concepts are learned and represented by means of a set of association rules that relate the presence of perceptual features to the fulfillment of a concept for a set of images. Since both the set of images where a perceptual feature appears and the set of images fulfilling a given concept are fuzzy, we use in fact fuzzy association rules for the learning model. The concepts...

On a fuzzy querying and data mining interface

Janusz Kacprzyk, Sławomir Zadrożny (2000)

Kybernetika

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In the paper an interface is proposed that combines flexible (fuzzy) querying and data mining functionality. The point of departure is the fuzzy querying interface designed and implemented previously by the present authors. It makes it possible to formulate and execute, against a traditional (crisp) database, queries containing imprecisely specified conditions. Here we discuss possibilities to extend it with some data mining features. More specifically, linguistic summarization of data...

Evolutionary algorithms and fuzzy sets for discovering temporal rules

Stephen G. Matthews, Mario A. Gongora, Adrian A. Hopgood (2013)

International Journal of Applied Mathematics and Computer Science

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A novel method is presented for mining fuzzy association rules that have a temporal pattern. Our proposed method contributes towards discovering temporal patterns that could otherwise be lost from defining the membership functions before the mining process. The novelty of this research lies in exploring the composition of fuzzy and temporal association rules, and using a multi-objective evolutionary algorithm combined with iterative rule learning to mine many rules. Temporal patterns...

Dual meaning of verbal quantities

Milan Mareš, Radko Mesiar (2002)

Kybernetika

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The aim of the paper is to summarize and interpret some ideas regarding effective processing of vague data. The main contribution of the submitted approach consists in respecting the fact that vague data can be decomposed into two parts. The numerical one, describing the quantitative value of such data, and the semantic one characterizing the qualitative structure of the vagueness included into them. This partition of vague verbal data leads to a significant simplification of their practical...