Fuzzy Kohonen clustering networks
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Cited by (240)
Mining event logs for knowledge discovery based on adaptive efficient fuzzy Kohonen clustering network
2020, Knowledge-Based SystemsCitation Excerpt :Due to such distinct advantages, FCM has been combined with other concepts to obtain more desirable results for large data in multi-dimensional space and noisy environments [17,18]. One of the most famous models is the fuzzy Kohonen clustering network (FKCN) [19] to take the best of KCN and FCM, which integrates the FCM into the learning rate and updating strategies of KCN. As a result, the neighborhood size and the learning rate can be updated automatically, while the cluster weights can be obtained by minimizing the objective function [20].
Imbalanced credit risk evaluation based on multiple sampling, multiple kernel fuzzy self-organizing map and local accuracy ensemble
2020, Applied Soft Computing JournalCitation Excerpt :Jardin used the quantification of temporal patterns to improve SOM to create the ensemble model that characterized the financial health of a set of companies, and on the use of an ensemble of incremental size maps to make forecasts [30]. Fuzzy Clustering Means (FCM) is a common algorithm for the improvement because of its calculation process like SOM [31,32]. Kvalsund and Ripon combined SOM and fuzzy set model, and assessed the impact of SOM used as a discriminant analysis function in a hybrid intelligent system for multi-factor analysis financial prediction [33].
BIM log mining: Exploring design productivity characteristics
2020, Automation in ConstructionCitation Excerpt :In order to make it more satisfactory, it becomes a research focus on interfacing between neural networks and fuzzy clustering by incorporating fuzzy membership values into the learning rate in neural networks [14]. By merging KCN and FCM, this kind of hybrid clustering method called fuzzy Kohonen clustering network (FKCN) is able to inherit advantages from both KCN and FCM and make up for shortcomings of each method [45]. To sum up, the superiority of FKCN is distinguished in three major ways: (1) It is capable of handling data with ambiguity and uncertainty; (2) It is not very susceptible to initial parameters; and (3) It can speed up the convergence rate with fewer training cycles.
A neuro-fuzzy Kohonen network for data stream possibilistic clustering and its online self-learning procedure
2018, Applied Soft Computing Journal