Abstract
This work proposes a new clustering algorithm named FINFCM by converting original data into fuzzy interval number (FIN) firstly, then it proofs F that denotes the collection of FINs is a lattice and introduce a novel metric distance based on the results from lattice theory as well as combining them with Fuzzy c-means clustering. The relevant mathematical background about lattice theory and the specific procedure which is used to construct FIN have been presented in this paper. Three evaluation indexes including Compactness, RandIndex and Precision are applied to evaluate the performance of FINFCM, FCM and HC algorithm in four experiments used UCI public datasets. The FINFCM algorithm has shown better clustering performance compared to other traditional clustering algorithms and the results are also discussed specifically.
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