Palaniswami, "Fuzzy c-Means Algorithms for Very Large Data," IEEE Trans. on Fuzzy Systems, vol. 20, no. 6, pp. 1130-1146, December 2012.T.C. Havens, J.C. Bezdek, C. Leckie, L.O. Hall, M. Palaniswami, Fuzzy c-me
We present a hotspot detection method based on the Extended Fuzzy C-Means (EFCM) algorithm for large (L) and very large (VL) datasets of events. Extensions of four VL-FCM algorithms are presented. We test our method applying these algorithms to an L dataset composed from the epicenters of...
Parallel Fuzzy c-Means Clustering for Large Data Sets Terence Kwok1, Kate Smith1, Sebastian Lozano2, and David Taniar1 1 School of Business Systems, Faculty of Information Technology, Monash University, Australia {terence.kwok, kate.smith, david.taniar}@infotech.monash.edu.au 2 Escuela Superior...
Clustering techniques such as fuzzy c-means and k-means clustering algorithms require initial memberships of data points in the process of clustering. Both clustering algorithms rely on the random assignment of memberships of genes to the clusters as the initialization process. As a result, ...
Comparison of K-Means and Fuzzy C-Means Data Mining Algorithms for Analysis of Management Information: An Open Source CaseFuzzy C-MEANS algorithmK-MEANSData Miningmanagement data analysisThis research presents the knowledge discovery using Data Mining from the organization and with a KPI management ...
c-means and fuzzy c-means clustering are two very popular image segmentation algorithms. While their implementation is straightforward, if realized naively it will lead to substantial overhead in execution time and memory consumption. Although these deficiencies could be ignored for small 2D images th...
Interpretability is the dominant feature of a fuzzy model in security-oriented fields. Traditionally fuzzy models based on expert knowledge have obtained well interpretation innately but imprecisely. Numerical data based fuzzy models perform well in prec
James CB. Pattern recognition with fuzzy objective function algorithms. Berlin: Springer; 2013. Google Scholar Hung YW, Chiu YH, Jou YC, Chen WH, Cheng KS. Bed posture classification based on artificial neural network using fuzzy c-means and latent semantic analysis. J Chin Inst Eng. 2015;...
It is challenging to extract the brain region from T2-weighted magnetic resonance infant brain images because conventional brain segmentation algorithms are generally optimized for adult brain images, which have different spatial resolution, dynamic changes of imaging intensity, brain size and shape from ...
very sensitive to the choice of the additional parameters needed by the PCM model. Timm et al. [13]–[15] proposed two possibilistic fuzzy clus- tering algorithms that can avoid the coincident cluster problem of PCM. In [13] and [14], the authors modified the PCM ob- ...