A clustering fuzzification algorithm based on ALM

In this paper, we propose a fuzzification method for clusters produced from a clustering process, based on Active Learning Method (ALM). ALM is a soft computing methodology which is based on a hypothesis claiming that human brain interprets information in pattern-like images. The proposed fuzzificat...

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Vydáno v:Fuzzy sets and systems Ročník 389; s. 93 - 113
Hlavní autoři: Javadian, Mohammad, Malekzadeh, Ahad, Heydari, Gholamali, Bagheri Shouraki, Saeed
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 15.06.2020
Témata:
Clustering fuzzification
Fuzzified clustering algorithms
Fuzzified DBSCAN
Fuzzified kmeans
Fuzzy clusters
Clustering fuzzification
Fuzzified DBSCAN
Fuzzified kmeans
Fuzzified clustering algorithms
Fuzzy clusters
ISSN:0165-0114, 1872-6801
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Shrnutí:In this paper, we propose a fuzzification method for clusters produced from a clustering process, based on Active Learning Method (ALM). ALM is a soft computing methodology which is based on a hypothesis claiming that human brain interprets information in pattern-like images. The proposed fuzzification method is applicable to all non-fuzzy clustering algorithms as a post process. The most outstanding advantage of this method is the ability to determine the membership degrees of each data to all clusters based on the density and shape of the clusters. It is worth mentioning that for existing fuzzy clustering algorithms such as FCM the membership degree is usually determined as a function of distance to the center of the clusters. In our proposed method, all data points of a cluster will play a role in order to determine the membership degrees. Consequently, the obtained membership degrees will depend on all of the data points of clusters, the amount of data points, and the density distribution of the clusters. Simulations prove the advantages of the proposed method.
ISSN:0165-0114
1872-6801
DOI:10.1016/j.fss.2019.10.013