An algorithm for computing the transitive closure of a fuzzy similarity matrix
Fuzzy Sets and Systems
A conceptual version of the K-means algorithm
Pattern Recognition Letters
An approximation algorithm for clustering graphs with dominating diametral path
Information Processing Letters
Integration of self-organizing feature map and K-means algorithm for market segmentation
Computers and Operations Research
Logic-oriented fuzzy clustering
Pattern Recognition Letters
An evolutionary technique based on K-means algorithm for optimal clustering in RN
Information Sciences—Applications: An International Journal
Information clustering based on fuzzy multisets
Information Processing and Management: an International Journal - Modelling vagueness and subjectivity in information access
Comparison of clustering methods for clinical databases
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
Cluster center initialization algorithm for K-means clustering
Pattern Recognition Letters
Clustering with a minimum spanning tree of scale-free-like structure
Pattern Recognition Letters
A clustering method to identify representative financial ratios
Information Sciences: an International Journal
| Hi-index | 12.05 |
In data analyzing, data is often presented as sequences. To partition the data sequences, we propose a sequence clustering system in which a fuzzy compatible relation is employed to show the similarity between any two sequences. Moreover, the max-min transitive closure is applied to transfer the fuzzy compatible relation into a fuzzy equivalence relation. It is found that the data sequences with more similar variations are clustered together by using the proposed clustering system. In that case, the sequences are partitioned easily and quickly into clusters.