Title
Perspective-oriented data analysis through the development of information granules of order 2.
Abstract
The problem of interest in this study is to describe and quantify a structure of data sets X1,X2,…,Xp with the use of a certain referential structure (composed of a collection of referential fuzzy sets) constructed on the basis of some previously available data X. The essence of the proposed approach is to carry out clustering in any Xi completed in a new granular feature space constructed with the aid of referential fuzzy sets. As a result, the clusters formed in Xi in this way emerge in the form of fuzzy sets of order-2. The lack of precision (variability) being associated with this description is quantified with the aid of entropy measure and directly relates the new structure with the notion of surprise (unexpectedness, interestingness) of the concepts and anomalies occurring in the data. Experimental studies are reported for synthetic data and real-world multivariable time series.
Year
DOI
Venue
2017
10.1016/j.ijar.2017.03.006
International Journal of Approximate Reasoning
Keywords
Field
DocType
Non-stationary data,Concept drift,Order-2 information granules,Reference information granules,Clustering,Context-oriented data analysis
Data mining,Data set,Fuzzy set,Synthetic data,Artificial intelligence,Surprise,Cluster analysis,Discrete mathematics,Feature vector,Multivariable calculus,Concept drift,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
85
1
0888-613X
Citations 
PageRank 
References 
1
0.37
4
Authors
4
Name
Order
Citations
PageRank
Abdullah Saeed Balamash11587.99
W. Pedrycz2139661005.85
Rami Al-hmouz332319.34
Ali Morfeq427517.38