Hierarchical clustering on metric lattice. (26th June 2020)

• Record Type: Journal Article
• Authors: Meng, Xiangyan; Liu, Muyan; Wu, Jingyi; Zhou, Huiqiu; Xu, Fei; Wu, Qiufeng
• Is Part Of: International journal of intelligent information and database systems. Volume 13:Number 1(2020)
• Page Start: 1
• Page End: 16
• Publication Date: 2020-06-26
• Subjects: fuzzy interval number -- FIN -- hierarchical clustering -- metric lattice -- public datasets -- compactness -- recall -- F1-measure; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

Abstract

This work proposes a new clustering algorithm named 'fuzzy interval number hierarchical clustering' (FINHC) by converting original data into fuzzy interval number (FIN) firstly, then it proves F that denotes the collection of FINs is a lattice and introduces a novel metric distance based on the results from lattice theory, as well as combining them with hierarchical clustering. The relevant mathematical background about lattice theory and the specific algorithm which is used to construct FIN have been presented in this paper. Three evaluation indexes including compactness, recall and F1-measure are applied to evaluate the performance of FINHC, hierarchical clustering (HC) k-means, k-medoids, density-based spatial clustering of applications with noise (DBSCAN) in six experiments used UCI public datasets and one experiment used KEEL public dataset. The FINHC algorithm shows better clustering performance compared to other traditional clustering algorithms and the results are also discussed specifically.