Paper Info
Title
Cluster-Volume-Based Merging Approach for Incrementally Evolving Fuzzy Gaussian Clustering—eGAUSS+
Abstract
In this article, a new dynamic merging approach for incrementally evolving clustering is presented. This means that the cluster partitions are incrementally learned on-line from streams of data. The criterion of merging is based on the comparison between the sum of volumes of two clusters that fulfill the criteria of a minimal number of samples in the cluster and the expected volume of the newly generated merged cluster. The newly generated merged cluster is conducted by using the weighted averaging of cluster centers and the calculation of the joint covariance matrix from the covariance matrices of the clusters. It has been shown that the proposed new evolving algorithm eGAUSS+ together with the new merging concept is very easy to implement, can work on higher-dimensional data sets, can perform all necessary computation on-line, and can produce reliable clusters.
Year
DOI
Venue
2020
10.1109/TFUZZ.2019.2931874
IEEE Transactions on Fuzzy Systems
Keywords
DocType
Volume
Data stream,dynamic merging,evolving clustering,evolving cluster models,incremental learning,volume of hyperellipsoids
Journal
28
Issue
ISSN
Citations 
9
1063-6706
3
PageRank 
References 
Authors
0.37
21
1
Name
Order
Citations
PageRank
Igor Skrjanc135452.47