[PDF] Top 20 Autonomous clustering using rough set theory
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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
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One Rough Intuitionistic Type 2 FCM Algorithm for Image Segmentation
... and set theory in 1965, Ruspini first introduced the theory of mould and partition in ...FCM clustering algorithm is suitable for image ...by using Bayes formula, then the maximum ... See full document
5
An Enhanced Clustering Technique Using Rough Set Approach for Discovering Improved and Unambiguous Groups of Users
... data. Rough set theory is one of the soft computing techniques which has a mathematical approach to uncertain and vague ...given set into two sets called lower approximations and upper ... See full document
6
A Computational Intelligence Technique for Effective and Early Diabetes Detection using Rough Set Theory
... The 21st century technology made it very easy to handle huge quantity of data as a part of storage and analysis. Trillions of databases are used to handle business management, scientific research, medical filed and ... See full document
5
ROUGH SET BASED CLUSTERING OF GENE EXPRESSION DATA: A SURVEY
... the clustering methods for gene expression data that are based on rough set ...that rough set based Clustering algorithms helps in identifying hidden pattern and providing ... See full document
5
Feature Subset Selection using Rough Sets for High Dimensional Data
... features, clustering-based method to remove redundant features and Rough Set Theory (RST) with greedy heuristics for feature subset ... See full document
5
Predictive modeling for telco customer churn using rough set theory
... data set. Classification, regression and clustering are the most common tasks in data ...apply rough set theory KDD method to classify and analyse customer churn in Telecommunication ... See full document
5
Color Image Segmentation using Rough Set based K Means Algorithm
... rough set theory is proposed, in which pixels are clustered according to the intensity and spatial features and then clusters are combined to get the results of final ...K-means clustering [1, ... See full document
6
Rough set approach for categorical data clustering
... on rough set theory, a mathematical formalism developed by ...of rough set theory for data mining is one approach that has proved successfully (Magnani, ...years, rough ... See full document
47
Application of kohonen neural network and rough approximation for overlapping clusters optimization
... The approach is based on the rough set theory that employs a soft clustering which can detects overlapped data from the data set and makes clustering as precise as possible,[r] ... See full document
15
Self organising transparent learning system
... A key concept in the field of pattern recognition is “uncertainty” [2], [3]. Uncertainty exists in our daily lives as well as in every discipline in science, engineering, and technology. Many actions have consequences ... See full document
254
Anomaly intrusion detection system using immune network with reduced network traffic features
... Shon et al., (2005) employed a GA for selecting proper TCP/IP packet fields to be applied to support vector learning in order to distinguish anomaly attacks from normal packets. Their results showed that their proposed ... See full document
28
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
Retrieving the Missing Information from Information Systems Using Rough Set, Covering Based Rough Set and Soft Set
... between rough set and covering based ...of rough set, attribute values are known but there are not enough attributes (or attribute domains are too coarse) to identify a single object by their ... See full document
5
Rough Filters in 𝐵𝐿 Algebras
... The set-oriented view focuses on the interpretation and characterization of members of rough ...and set- oriented views are useful in the understanding and application of the theory of ... See full document
14
Rough Set Theory Based Entropy Approach For Feature Selection In Adaptive Intrusion Detection System
... sets, rough sets gained a massive attraction particularly in the field of machine learning and artificial ...of rough set was initially proposed by the computer scientist Zdzisław ...fuzzy ... See full document
7
A New Method of Translating Covering Rough Set into Classical Rough Set
... sets theory is a mathematical tool for dealing with uncertain data which proposed by Pawlak-a Polish scholar ...uncertainties, rough set theory doesn't need any prior information, and it has ... See full document
7
A map task view Generation strategy Based on Rough Set Theory
... The traditional parallel reduction strategy assumes place all objects into the memory at one time. Yet this is not suitable for large-scale task view sets in Gloud storage system [10]. By using the MapReduce ... See full document
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Rough set theory based prognostic classification models for hospice referral
... An important limitation of this study is that patient- specific disease progression over time is not considered, in part due to the static nature of the data set used. Future research must address the temporal ... See full document
18
"Possible Definitions of an ’A Priori’ Granule in General Rough Set Theory" by A. Mani
... • Interestingly many semantics of general RST do not make use of any ’rough inclusions’ at all as their intent is not to describe ’roughly equivalent objects’. Example: Cattaneo’s BZ-algebras and variants. • In ... See full document
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