The advantages of FCM algorithm are that it is mainly applied in point data cluster and can't directly process relational data, for which the paper proposes a clustering algorithm in data mining based on web log. Firstly, the paper improves FCM algorithm which makes it can process relational data, and makes robustness improvement on the algorithm. Then, the traditional FCM algorithm needs to de...

Fuzzy C-means (FCM) is an unsupervised clustering technique that is often used for the unsuper-vised segmentation of multivariate images. In traditional FCM the clustering is based on spectral information only and the geometrical relationship between neighbouring pixels is not used in the clustering procedure. In this paper, the spatially guided FCM (SG-FCM) algorithm is presented which segment...

Citrus fruits have been used as edible fruit and a component of traditional medicine for various diseases including cancer since ancient times. Herein, we investigated the anticancer activity of flavonoids of Citrus unshiu Marc. (FCM) focusing on anti-metastatic effects. We prepared FCM and performed experiments using MDA-MB-231 human breast cancer cells. FCM inhibited TNF-induced cancer cell a...

Obesity causes metabolic syndrome disorders that are detrimental to health. The current study examined the effects of intermittent fasting (IF), fermented camel milk (FCM), and incorporating 10% Sukkari date (FCM-D) on weight loss, blood profile, antioxidant status in obese rats for 6 weeks. Subsequently, leptin adiponectin levels histopathological examination adipose tissue were carried out. R...

This paper presents an algorithm, called the modified suppressed fuzzy c-means (MS-FCM), that simultaneously performs clustering and parameter selection for the suppressed fuzzy c-means (S-FCM) algorithm proposed by [Fan, J.L., Zhen, W.Z., Xie, W.X., 2003. Suppressed fuzzy c-means clustering algorithm. Pattern Recognition Lett. 24, 1607–1612]. The proposed algorithm is computationally simple, a...

This paper considers the problem of partitioning noisy images into different regions by fuzzy clustering approach. Based on two fuzzy c-means (FCM) algorithms (FCM S1 and FCM S2), we propose four adaptive algorithms (FCM S11, FCM S12, FCM S21 and FCM S22) which utilize the high correlation of image pixels to increase the algorithms’ robustness to noise. Unlike existing algorithms, our algorithm...

OBJECTIVE Cellular microparticles (MP) are promising biomarkers in many pathological situations. Although flow cytometry (FCM) is widely used for their measurement, it has raised controversies because the smallest MP size falls below the detection limit of standard FCM (sd-FCM). Following recent technological improvements leading to high sensitivity FCM (hs-FCM), our objectives were (1) to eval...

This paper presents a hybrid differential evolution, particle swarm optimization and fuzzy c-means clustering algorithm called DEPSO-FCM for image segmentation. By the use of the differential evolution (DE) algorithm and particle swarm optimization to solve the FCM image segmentation influenced by the initial cluster centers and easily into a local optimum. Empirical results show that the propo...

The triple-frequency linear combination method can provide combinations with different characteristics and is one of the important methods to improve performance navigation services. Due large number performances, combinatorial clustering optimization very important, efficiency manual screening low. Firstly, based on basic model, objective equations are derived. Secondly, fuzzy c-means (FCM) al...