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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| Veröffentlicht in: | Fuzzy sets and systems Jg. 389; S. 93 - 113 |
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15.06.2020
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| ISSN: | 0165-0114, 1872-6801 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Malekzadeh, Ahad Javadian, Mohammad Heydari, Gholamali Bagheri Shouraki, Saeed |
| Author_xml | – sequence: 1 givenname: Mohammad orcidid: 0000-0002-7054-421X surname: Javadian fullname: Javadian, Mohammad email: m.javadian@kut.ac.ir organization: Computer Department, Faculty of Information Technology, Kermanshah University of Technology, Kermanshah, Iran – sequence: 2 givenname: Ahad surname: Malekzadeh fullname: Malekzadeh, Ahad organization: Department of Computer Science and Statistics, Faculty of Mathematics, K.N. Toosi University of Technology, Tehran, Iran – sequence: 3 givenname: Gholamali surname: Heydari fullname: Heydari, Gholamali organization: Electrical Engineering Department, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran – sequence: 4 givenname: Saeed surname: Bagheri Shouraki fullname: Bagheri Shouraki, Saeed organization: Electrical Engineering Department, Sharif University of Technology, Tehran, Iran |
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| Keywords | Clustering fuzzification Fuzzified DBSCAN Fuzzified kmeans Fuzzified clustering algorithms Fuzzy clusters |
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