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

摘要: Abstract In this paper, we propose a novel density-based fuzzy clustering algorithm based on Active Learning Method (ALM), which is methodology of soft computing inspired by some hypotheses claiming that human brain interprets information in pattern-like images rather than numerical quantities. The proposed algorithm, Fuzzy Unsupervised (FUALM), performed two main phases. First, each data point spreads the feature space just like an ink drop sheet paper. As result process, densely connected patterns are formed represent clusters. second phase, fuzzifying process applied order to summarize effects all members cluster. Finding arbitrary shaped clusters, noise robustness and proposing clusters advantages our algorithm. described full details its performance evaluated compared with well-known algorithms synthetic real-world datasets.