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A novel density-based fuzzy clustering algorithm for low dimensional feature space

Javadian, M ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1016/j.fss.2016.10.012
  3. Abstract:
  4. In this paper, we propose a novel density-based fuzzy clustering algorithm based on Active Learning Method (ALM), which is a methodology of soft computing inspired by some hypotheses claiming that human brain interprets information in pattern-like images rather than numerical quantities. The proposed clustering algorithm, Fuzzy Unsupervised Active Learning Method (FUALM), is performed in two main phases. First, each data point spreads in the feature space just like an ink drop that spreads on a sheet of paper. As a result of this process, densely connected ink patterns are formed that represent clusters. In the second phase, a fuzzifying process is applied in order to summarize the effects of all members of each cluster. Finding arbitrary shaped clusters, noise robustness and proposing fuzzy clusters are some of the advantages of our proposed clustering algorithm. The algorithm is described in full details and its performance is evaluated and compared with well-known clustering algorithms on synthetic and real-world datasets. © 2016 Elsevier B.V
  5. Keywords:
  6. Fuzzy unsupervised active learning method (FUALM) ; Artificial intelligence ; Fuzzy clustering ; Learning algorithms ; Learning systems ; Numerical methods ; Soft computing ; Active learning methods ; Density-based clustering ; Feature space ; Fuzzy data ; Low dimensional ; Noise robustness ; Numerical quantity ; Real-world datasets ; Clustering algorithms
  7. Source: Fuzzy Sets and Systems ; Volume 318 , 2017 , Pages 34-55 ; 01650114 (ISSN)
  8. URL: https://www.sciencedirect.com/science/article/pii/S0165011416303438