Results 31 to 40 of about 23,287,312 (305)

Approximating a similarity matrix by a latent class model: A reappraisal of additive fuzzy clustering [PDF]

, 2009
Let Q be a given n×n square symmetric matrix of nonnegative elements between 0 and 1, similarities. Fuzzy clustering results in fuzzy assignment of individuals to K clusters.
Braak, C.J.F., ter, Kourmpetis, Y.I.A., Kiers, H.A.L., Bink, M.C.A.M.   +3 more
core   +1 more source

On Fuzzy c-Means for Data with Tolerance

Journal of Advanced Computational Intelligence and Intelligent Informatics, 2006
This paper presents two new clustering algorithms which are based on the entropy regularized fuzzyc-means and can treat data with some errors. First, the tolerance is formulated and introduce into optimization problems of clustering. Next, the problems are solved using Kuhn-Tucker conditions. Last, the algorithms are constructed based on the results of
Ryuichi Murata, Yasunori Endo, Hideyuki Haruyama, Sadaaki Miyamoto   +3 more
openaire   +1 more source

Hybrid image segmentation using Fuzzy C-Means and gravitational search algorithm [PDF]

, 2012
In this paper, we propose a new hybrid approach for image segmentation. The proposed approach exploits spatial fuzzy c-means for clustering image pixels into homogeneous regions.
Ullah Sheikh, Usman, As'ari, Muhammad Amir, Emadaldin Mozafari Majd, M. A. As'ari, As'ari, M. A., Mozafari Majd, Emadaldin, U. U. Sheikh, Abu-Bakar, S. A. R., Syed Abu Bakar, Syed Abdul Rahman, S. A. R. Abu-Bakar, Sheikh, U.U., Majd, E. M.   +11 more
core   +1 more source

Intuitionistic Fuzzy Possibilistic C Means Clustering Algorithms

Advances in Fuzzy Systems, 2015
Intuitionistic fuzzy sets (IFSs) provide mathematical framework based on fuzzy sets to describe vagueness in data. It finds interesting and promising applications in different domains. Here, we develop an intuitionistic fuzzy possibilistic C means (IFPCM)
Arindam Chaudhuri
doaj   +1 more source

Applying the possibilistic C-means algorithm in kernel-induced spaces [PDF]

, 2010
In this paper, we study a kernel extension of the classic possibilistic c-means. In the proposed extension, we implicitly map input patterns into a possibly high-dimensional space by means of positive semidefinite kernels. In this new space, we model the
Masulli, F., M. Filippone, Filippone, M., ROVETTA, STEFANO, MASULLI, FRANCESCO, Rovetta, S.   +5 more
core   +1 more source

Data Mining Algorithm for Cloud Network Information Based on Artificial Intelligence Decision Mechanism

IEEE Access, 2020
Due to the rapid development of information technology and network technology, there is a lot of data, but the phenomenon of lack of knowledge is becoming more and more serious.
Yuan Huang, Zhe Cheng, Qianyu Zhou, Yuxing Xiang, Ruixiao Zhao   +4 more
doaj   +1 more source

An improved fuzzy clustering image segmentation algorithm combining spatial information

Xi'an Gongcheng Daxue xuebao, 2021
In order to improve the ability of fuzzy C-means (FCM) clustering algorithm to suppress noise, an improved fuzzy clustering image segmentation algorithm was proposed.
Xudong LIU, Yunhong LI, Haitao QU, Xueping SU, Rongrong XIE   +4 more
doaj   +1 more source

Fuzzy clustering of time series gene expression data with cubic-spline [PDF]

, 2013
Data clustering techniques have been applied to ex- tract information from gene expression data for two decades. A large volume of novel clustering algorithms have been developed and achieved great success.
Ali, Akhtar, Wang, Yu, Angelova, Maia
core   +1 more source

Modified Probabilistic Intuitionistic Fuzzy c-Means Clustering Algorithm: MPIFCM

, 2022
The recently reported ‘Improved Probabilistic Intuitionistic Fuzzy c-Means (IPIFCM) algorithm’ is a computationally efficient algorithm that does fuzzy clustering based on Probabilistic Euclidean Distance measure (PEDM).
Muhuri, Pranab K., Chakraborty, Debanjan, Varshney, Ayush K.,, Lohani, Q.M. Danish   +3 more
core   +1 more source

APLIKASI METODE FUZZY C-MEANS UNTUK MENENTUKAN TINGKAT PENGANGGURAN

Barekeng, 2017
Pada penelitian ini Algoritma Fuzzy C-Means digunakan untuk menentukan tingkat pengangguran pada 11 kabupaten di Provinsi Maluku. Variabel yang digunakan dalam penelitian ini adalah Jumlah Penduduk, Tingkat Partisipasi Angkatan Kerja (TPAK), Jumlah ...
Dorteus L. Rahakbauw, Lexy J. Sinay, Vilomina Enus   +2 more
doaj   +1 more source