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[PDF] Top 20 A Distributed Clustering Approach for Heterogeneous Environments Using Fuzzy Rough Set Theory

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A Distributed Clustering Approach for Heterogeneous Environments  Using Fuzzy Rough Set Theory

A Distributed Clustering Approach for Heterogeneous Environments Using Fuzzy Rough Set Theory

... are distributed both geographically and ...centralizing distributed data sets before analyzing them is undoubtedly ...for clustering distributed data which takes into account privacy and ... See full document

14

A NOVEL APPROACH OF ROUGH SET ANALYSIS IN DISTRIBUTED DATA MINING

A NOVEL APPROACH OF ROUGH SET ANALYSIS IN DISTRIBUTED DATA MINING

... Rough set theory is a new mathematical approach to imperfect ...the fuzzy set theory proposed by Zadeh .Rough set theory proposed by Zpawlak presents ... See full document

9

Color Image Segmentation using Rough Set based K Means Algorithm

Color Image Segmentation using Rough Set based K Means Algorithm

... a rough set approach for color image segmentation that can automatically segment an image to its constituents ...images using intensity information along with neighborhood ...K-means ... See full document

6

Autonomous clustering using rough set theory

Autonomous clustering using rough set theory

... of clustering is to partition a data set into classes such that within-class homogeneity is high and between-class homogeneity ...standard clustering techniques, including agglomerative hierarchical ... See full document

33

Empirical analysis of rough set categorical clustering techniques based on rough purity and value set

Empirical analysis of rough set categorical clustering techniques based on rough purity and value set

... a clustering technique also relies tremendously on specific data ...several clustering techniques developed to combine objects of same characteristics, however the implementation of them is challenging due ... See full document

54

Anomaly-based intrusion detection using fuzzy rough clustering

Anomaly-based intrusion detection using fuzzy rough clustering

... Most of machine learning approaches are based on supervised learning, and have following problems [2]: 1) a large volume of training data should be collected and classified manually; 2) the performance of the IDS depends ... See full document

6

Study on Software Process Metrics using Data Mining Tool  A Rough Set Theory Approach

Study on Software Process Metrics using Data Mining Tool A Rough Set Theory Approach

... reducted set mean and the non- reducted data set vales, the mean value of original code , modified code a, accepted without modification and the production of LOC is directly reflected based on the above ... See full document

5

Attribute Reduction With Imputation Of Missing Data Using Fuzzy Rougsh Set

Attribute Reduction With Imputation Of Missing Data Using Fuzzy Rougsh Set

... data. Fuzzy rough attribute reduction with imputation (FRARI) algorithm has been proposed for attribute reduction with missing value imputation using fuzzy rough set based ... See full document

6

Rough clustering for web transactions

Rough clustering for web transactions

... Recently, there are many previous researches that discuss about web usage mining. Discovery of user navigation pattern of Web page is important in order to identify potential customers for marketing strategy and build ... See full document

41

Sheaf Theory Approach to Distributed Applications: Analysing Heterogeneous Data in Air Traffic Monitoring

Sheaf Theory Approach to Distributed Applications: Analysing Heterogeneous Data in Air Traffic Monitoring

... Main pillar of the whole construction is the notion of data space, the crucial feature of which is a topological space. Fundamental goal of it is to simply converting the collection of points in data space into a ... See full document

6

Applications of Rough Sets Theory in Data Preprocessing for Knowledge Discovery

Applications of Rough Sets Theory in Data Preprocessing for Knowledge Discovery

... Discretization is the process to transform continuous features into qualitative features. Firstly, continuous feature values are divided into subintervals. Then, each interval is mapped to a discrete symbol (categorical, ... See full document

6

ARIMA METHOD WITH THE SOFTWARE MINITAB AND EVIEWS TO FORECAST INFLATION IN 
SEMARANG INDONESIA

ARIMA METHOD WITH THE SOFTWARE MINITAB AND EVIEWS TO FORECAST INFLATION IN SEMARANG INDONESIA

... intuitionistic fuzzy membership degree. Atanassovs intuitionistic fuzzy membership function [7] is recently is used to verify the best threshold for gray-level image ...segmentation using multiple ... See full document

10

Estimation of Failure Probability Using Fault Tree Analysis and Fuzzy Logic for CO2 Transmission

Estimation of Failure Probability Using Fault Tree Analysis and Fuzzy Logic for CO2 Transmission

... the same data will be generated using this method. For our research different experts are bought into indirect interaction regarding the discussion on the probability if an event. A defined advisory table is given ... See full document

5

E-BEENISH: Enhanced Balanced Energy Efficient Network Integrated Super Heterogeneous Protocol For WSNAbhijit Singh, Shashi B. Rana

E-BEENISH: Enhanced Balanced Energy Efficient Network Integrated Super Heterogeneous Protocol For WSNAbhijit Singh, Shashi B. Rana

... the approach that they have considered in the cluster head ...the approach for selecting the cluster head, the probabilistic formula has been incorporated with the modifications that lead to enhanced ... See full document

5

A note on inequalities and critical values of fuzzy rough variables

A note on inequalities and critical values of fuzzy rough variables

... the fuzzy set of a fuzzy event can be given, which can be seen as a fuzzy rough ...of fuzzy rough sets by constructing a pair of upper and lower approximation operators of ... See full document

14

3. Rough convergence of a sequence of fuzzy numbers

3. Rough convergence of a sequence of fuzzy numbers

... of rough convergence of a sequence of fuzzy ...the set of rough limit and the extreme limit points of a sequence of fuzzy ...the rough limit set of this ...the ... See full document

7

A Rough Set Approach towards Analysis of Cosmetic Data

A Rough Set Approach towards Analysis of Cosmetic Data

... residual set of attributes after feature selection is termed as ...minimal set of ...the set of all reducts by considering only the common ...of rough set method useful for deriving a ... See full document

5

An Enhanced Clustering Technique Using Rough Set Approach for Discovering Improved and Unambiguous Groups of Users

An Enhanced Clustering Technique Using Rough Set Approach for Discovering Improved and Unambiguous Groups of Users

... Clustering is widely used technique in data mining applications. It groups the objects based on similarities among them. To make recommendation effective, web users are compared with available information about ... See full document

6

Online Full Text

Online Full Text

... The theory of rough sets has been specially designed to handle data imperfections same as in fuzzy ...logic. Rough sets remove superfluous information by examining attribute ...to set ... See full document

5

Performance Enhancement of Robust Rough Fuzzy Clustering using Silhouette Index

Performance Enhancement of Robust Rough Fuzzy Clustering using Silhouette Index

... gene clustering algorithm is proposed to group genes from microarray ...feature set to build the classifiers for cancer ...sets using the predictive accuracy of naive Bayes classifier, K-nearest ... See full document

6

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