Results 311 to 320 of about 948,152 (383)
Some of the next articles are maybe not open access.
Journal of Intelligent & Fuzzy Systems, 2013
Many variants of fuzzy c-means (FCM) clustering method are applied to crisp numbers but only a few of them are extended to non-crisp numbers, mainly due to the fact that the latter needs complicated equations and exhausting calculations. Vector form of fuzzy c-means (VFCM), proposed in this paper, simplifies the FCM clustering method applying to non ...
Hadi, Mahdipour +2 more
openaire +1 more source
Many variants of fuzzy c-means (FCM) clustering method are applied to crisp numbers but only a few of them are extended to non-crisp numbers, mainly due to the fact that the latter needs complicated equations and exhausting calculations. Vector form of fuzzy c-means (VFCM), proposed in this paper, simplifies the FCM clustering method applying to non ...
Hadi, Mahdipour +2 more
openaire +1 more source
Iteratively Reweighted Algorithm for Fuzzy $c$-Means
IEEE transactions on fuzzy systems, 2022Fuzzy $c$-means method (FCM) is a popular clustering method, which uses alternating iteration algorithm to update membership matrix $\mathbf {F}$ and center matrix $\mathbf {M}$ of $d \times c$ size.
Jingjing Xue +3 more
semanticscholar +1 more source
NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society, 2008
Clustering streaming data presents the problem of not having all the data available at one time. Further, the total size of the data may be larger than will fit in the available memory of a typical computer. If the data is very large, it is a challenge to apply fuzzy clustering algorithms to get a partition in a timely manner. In this paper, we present
P. Hore +3 more
openaire +1 more source
Clustering streaming data presents the problem of not having all the data available at one time. Further, the total size of the data may be larger than will fit in the available memory of a typical computer. If the data is very large, it is a challenge to apply fuzzy clustering algorithms to get a partition in a timely manner. In this paper, we present
P. Hore +3 more
openaire +1 more source
Hybrid Missing Value Imputation Algorithms Using Fuzzy C-Means and Vaguely Quantified Rough Set
IEEE transactions on fuzzy systems, 2021In real cases, missing values tend to contain meaningful information that should be acquired or should be analyzed before the incomplete dataset is used for machine learning tasks.
Daiwei Li +5 more
semanticscholar +1 more source
Variable Width Rough-Fuzzy c-Means
2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2017The richness of soft clustering algorithms in the scientific literature reflects from one side the complexity of the underlying problem and from the other the many attempts that have been made to preserve interpretability while modeling vagueness through different theories.
Ferone, Alessio +2 more
openaire +2 more sources
Fuzzy C-Means-based Isolation Forest
Applied Soft Computing, 2021The problem of finding anomalies (outliers) in databases is one of the most important issues in modern data analysis. One of the reasons is the occurrence of this issue in almost every type of database, including numerical, categorical, time, mixed, or ...
Paweł Karczmarek +3 more
semanticscholar +1 more source
Suppressed fuzzy c-means clustering algorithm
Pattern Recognition Letters, 2003Summary: Based on the defect of rival checked fuzzy \(c\)-means clustering algorithm, a new algorithm: suppressed fuzzy \(c\)-means clustering algorithm is proposed. The new algorithm overcomes the shortcomings of the original algorithm, establishes more natural and more reasonable relationships between hard \(c\)-means clustering algorithm and fuzzy \(
Fan, Jiu-Lun +2 more
openaire +2 more sources
Bi-criteria fuzzy c-means analysis
Fuzzy Sets and Systems, 1994zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wang, Hsiao-Fan +2 more
openaire +2 more sources
Improving Fuzzy C-Means Algorithm using Particle Swarm Optimization and Fuzzy C-Means++
2019 International Conference on Information and Digital Technologies (IDT), 2019The aim of the paper is to improve fuzzy particle swarm optimization. Our modification consists of modifying the hybrid fuzzy particle swarm optimization by replacing the random initialization of centers in the c-means fuzzy section with the FCM++ algorithm.
Olga Chovancova +2 more
openaire +1 more source
Categorical fuzzy entropy c-means
2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2020Hard and fuzzy clustering algorithms are part of the partition-based clustering family. They are widely used in real-world applications to cluster numerical and categorical data. While in hard clustering an object is assigned to a cluster with certainty, in fuzzy clustering an object can be assigned to different clusters given a membership degree.
Djiberou Mahamadou, Abdoul Jalil +3 more
openaire +2 more sources