Results 51 to 60 of about 172,977 (352)
Fuzzy Relational Fixed Point Clustering [PDF]
International Journal of Computational Intelligence Systems, 2009 The proposed relational fuzzy clustering method, called FRFP ( fuzzy relational fixed point), is based on determining a fixed point of a function of the desired membership matrix. The ethod is compared to other relational clustering methods.
Roelof K. Brouwer
doaj +1 more source
Probabilistic clustering algorithms for fuzzy rules decomposition [PDF]
, 2007 The fuzzy c-means (FCM) clustering algorithm is the best known and used method in fuzzy clustering and is generally applied to well defined set of data. In this paper a generalized Probabilistic fuzzy c-means (FCM) algorithm is proposed and applied to
Igrejas, Getúlio, Salgado, Paulo
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Multimodal Data‐Driven Microstructure Characterization
Advanced Engineering Materials, EarlyView.A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang +4 more
wiley +1 more source
Automatic Feature Set Selection for Merging Image Segmentation Results Using Fuzzy Clustering [PDF]
, 2005 The image segmentation performance of clustering algorithms is highly dependent on the features used and the type of objects contained in the image, which limits the generalization ability of such algorithms.
Ali, Ameer +2 more
core
Median evidential c-means algorithm and its application to community detection [PDF]
, 2015 Median clustering is of great value for partitioning relational data. In this paper, a new prototype-based clustering method, called Median Evidential C-Means (MECM), which is an extension of median c-means and median fuzzy c-means on the theoretical ...
Liu, Zhun-Ga +3 more
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A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science
Advanced Engineering Materials, EarlyView.We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann +8 more
wiley +1 more source
fcvalid: An R Package for Internal Validation of Probabilistic and Possibilistic Clustering
Sakarya University Journal of Computer and Information Sciences, 2020 In exploratory data analysis and machine learning, partitioning clustering is a frequently used unsupervised learning technique for finding the meaningful patterns in numeric datasets.
Zeynel Cebeci
doaj +1 more source
Comparing clustering models in bank customers: Based on Fuzzy relational clustering approach [PDF]
Accounting, 2016 Clustering is absolutely useful information to explore data structures and has been employed in many places. It organizes a set of objects into similar groups called clusters, and the objects within one cluster are both highly similar and dissimilar with
Ayad Hendalianpour +2 more
doaj +1 more source
, 2003 We develop an effective nonhierarchical data clustering method using an analogy to the dynamic coarse graining of a stochastic system. Analyzing the eigensystem of an interitem transition matrix identifies fuzzy clusters corresponding to the metastable ...
A. Pothen +17 more
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Clustering outdoor soundscapes using fuzzy ants [PDF]
, 2008 A classification algorithm for environmental sound recordings or "soundscapes" is outlined. An ant clustering approach is proposed, in which the behavior of the ants is governed by fuzzy rules.
Berglund, B. +4 more
core +2 more sources