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- research-articleJuly 2015
Fuzzy joint points based clustering algorithms for large data sets
Fuzzy Sets and Systems (FSTS), Volume 270, Issue CJuly 2015, Pages 111–126https://doi.org/10.1016/j.fss.2014.08.004The fuzzy joint points (FJP) method is one of the successful fuzzy approaches to density-based clustering. Besides the basic FJP method, there are other methods based on the FJP approach such as, Noise-Robust FJP (NRFJP), and Fuzzy Neighborhood DBSCAN (...
- research-articleMay 2011
Time series labeling algorithms based on the K-nearest neighbors' frequencies
Expert Systems with Applications: An International Journal (EXWA), Volume 38, Issue 5May, 2011, Pages 5028–5035https://doi.org/10.1016/j.eswa.2010.09.147Graphical abstractDisplay Omitted Research highlights New time series labeling algorithms based on clustering are proposed. The K-NN rule is applied to smooth the volatility of series. The algorithms decrease error magnitudes in statistically with α=...
- articleDecember 2009
Robustness of density-based clustering methods with various neighborhood relations
Fuzzy Sets and Systems (FSTS), Volume 160, Issue 24December, 2009, Pages 3601–3615https://doi.org/10.1016/j.fss.2009.06.012Cluster analysis is one of the most crucial techniques in statistical data analysis. Among the clustering methods, density-based methods have great importance due to their ability to recognize clusters with arbitrary shape. In this paper, robustness of ...
- articleOctober 2007
A new unsupervised approach for fuzzy clustering
Fuzzy Sets and Systems (FSTS), Volume 158, Issue 19October, 2007, Pages 2118–2133https://doi.org/10.1016/j.fss.2007.02.019In this paper, a new level-based (hierarchical) approach to the fuzzy clustering problem for spatial data is proposed. In this approach each point of the initial set is handled as a fuzzy point of the multidimensional space. Fuzzy point conical form, ...