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Proceedings ArticleDOI

The Fuzzy Rough Sets and Algorithm of Fuzzy Rough Clustering Based on Grid

19 May 2009-Vol. 4, pp 538-542
TL;DR: The algorithm improved speed, reliability and accuracy of TCM tongue diagnosis, also met the requirements of intellectualization and digitization.
Abstract: The paper studied a new theory of fuzzy rough sets, and presented a method to approximately estimate objects in a range. It presented an algorithm of Fuzzy Rough Clustering Based on Grid by the theory. The algorithm extracts condensation points by the theory of fuzzy rough sets, and quarters the data space layer by layer, and softens the edge of the dense block by drawing condensation points in the borders. The tongue diagnosis system is a big, complex one, its data is of great amount and the data cluster has uncertainty. The algorithm has been put into use in rules mining of Traditional Chinese Medicine (TCM) tongue diagnosis system. The application result indicated: The algorithm speeded up cluster by fuzzy grid dividing, saved a lot of time than traditional fuzzy cluster algorithm. The algorithm improved speed, reliability and accuracy of TCM tongue diagnosis, also met the requirements of intellectualization and digitization.
Citations
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Posted ContentDOI
03 Jul 2015-viXra
TL;DR: This paper discusses about rough sets and fuzzy rough sets with its applications in data mining that can handle uncertain and vague data so as to reach at meaningful conclusions.
Abstract: Rough set theory is a new method that deals with vagueness and uncertainty emphasized in decision making. The theory provides a practical approach for extraction of valid rules fromdata.This paper discusses about rough sets and fuzzy rough sets with its applications in data mining that can handle uncertain and vague data so as to reach at meaningful conclusions.

8 citations


Cites methods from "The Fuzzy Rough Sets and Algorithm ..."

  • ...A good clustering algorithm should possess expansibility, fast process high dimensions of data, and isinsensitive to noise, so fuzzy rough sets are used to handle this [10]....

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Proceedings ArticleDOI
01 Dec 2017
TL;DR: This paper discusses about the hybrid model of rough sets with relational maps to represent the mapping between consistent set of attributes of the dataset to decision classes and shows how Neutrosophic relational maps gives better results as compared to Fuzzy relational maps by considering indeterminate relations.
Abstract: Rough set theory is a new mathematical approach for uncertain knowledge. The main advantage of using rough sets is that it does not need any additional or prior information about data. This paper discusses about the hybrid model of rough sets with relational maps to represent the mapping between consistent set of attributes of the dataset to decision classes. The proposed model is tested on 7 led dataset where the rough sets are implemented to get the important reducts of the attributes for decision making and relational maps are used to map reducts with the decision classes. The two extensions of relational maps: Fuzzy relational maps and Neutrosophic relational maps are implemented in the dataset and is shown how Neutrosophic relational maps gives better results as compared to Fuzzy relational maps by considering indeterminate relations.
References
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Journal ArticleDOI
TL;DR: This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.
Abstract: Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.

7,185 citations

Journal ArticleDOI
TL;DR: A program for approximate classification--based on the rough set concept-- has been developed and aplied for computer-assisted medical diagnosis and results of computation are briefly discussed.
Abstract: This article contains a new concept of approximate analysis of data, based on the idea of a “rough” set. The notion of approximate (rough) description of a set is introduced and investigated. The application to medical data analysis is shown as an example.

485 citations

Journal ArticleDOI
01 Aug 1977
TL;DR: It is demonstrated that there exist classes of global optimization problems for which the probability of obtaining a solution is greater for the proposed model than for multiple local optimizations.
Abstract: A model for finding the local optima of a multimodal function defined in a region A ? Rn is proposed. The method uses a local optimizer which is started from a number of points sampled in A. In order to reduce the number of function evaluations needed to reach the local optima, the parallel local search processes are stopped repeatedly, the working points clustered, and a reduced number of processes from each cluster resumed. A direct nonhierarchical cluster analysis technique is presented. The dissimilarity measure used is the Euclidean distance between points. Clusters are grown from seed points. The number of required distance evaluations is less than or equal to c(n-1), where n is the number of points and c is the number of clusters arrived at. Thresholds are determined by the point density in a body which in turn is determined by the given points. The covariance matrix is diagonalized, and a decision on the dimensionality of the space containing the points can be made. The volume of the body is proportional to the square root of the product of the corresponding eigenvalues. The performance of the clustering analysis technique is illustrated. It is demonstrated that there exist classes of global optimization problems for which the probability of obtaining a solution is greater for the proposed model than for multiple local optimizations. Some experiences gained from using the model are reported.

98 citations


"The Fuzzy Rough Sets and Algorithm ..." refers methods in this paper

  • ...Many clustering algorithms have been presented and they were divided into four types: divide-based clustering algorithm([1]), density-based one([2]), layer-based one([3]) and grid-based one([4-5])....

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Proceedings ArticleDOI
08 Sep 1996
TL;DR: A new robust algorithm that estimates the prototype parameters of a given structure from a possibly noisy data set by dynamically estimates a scale parameter and the weights/memberships associated with each data point, and softly rejects outliers based on these weights.
Abstract: In this paper, we propose a new robust algorithm that estimates the prototype parameters of a given structure from a possibly noisy data set. The new algorithm has several attractive features. It does not make any assumptions on the proportion of noise in the data set. Instead, it dynamically estimates a scale parameter and the weights/memberships associated with each data point, and softly rejects outliers based on these weights. The algorithm essentially optimizes a density criterion, since it tries to minimize the size while maximizing the cardinality. Moreover, the proposed algorithm is computationally simple, and can be extended to perform parameter estimation when the data set consists of multiple clusters.

27 citations


"The Fuzzy Rough Sets and Algorithm ..." refers methods in this paper

  • ...Many clustering algorithms have been presented and they were divided into four types: divide-based clustering algorithm([1]), density-based one([2]), layer-based one([3]) and grid-based one([4-5])....

    [...]

Journal Article
TL;DR: Experimental results confirm that the execution efficiency of CGDCP is much better than a traditional climbing hill algorithm and the Clique algorithm and overcomes the traditional shortcomings of the grid-based clustering method's quality debasement.
Abstract: A new kind of clustering algorithm called CGDCP is presented.The creativity of CGDCP is capturing the shape of data space by condensation points,and then using grid-based and density-based clustering methods based on the theories of a climbing hill algorithm and connectivity to deal with the data.CGDCP retains the good features of grid-based and density-based clustering methods and overcomes the traditional shortcomings of the grid-based clustering method's quality debasement resulting from little or no consideration of data distribution when partitioning the grids.Experimental results confirm that the execution efficiency of CGDCP is much better than a traditional climbing hill algorithm and the Clique algorithm.

8 citations


"The Fuzzy Rough Sets and Algorithm ..." refers methods in this paper

  • ...Many clustering algorithms have been presented and they were divided into four types: divide-based clustering algorithm([1]), density-based one([2]), layer-based one([3]) and grid-based one([4-5])....

    [...]