A key aspect in extracting quantitative information from FMI logs is to segment the FMI image to get image of layers. In this paper, an automatic method based on FCM clustering and Otsu thresholding is introduced in order to extract quantitative information from FMI images. All pixels are clustered using FCM clustering algorithm at the f irst step. The second step uses KNN for other clustering....

This paper introduces modified versions of the K-Means (KM) and Moving K-Means (MKM) clustering algorithms, called the Two-Dimensional K-Means (2D-KM) and Two-Dimensional Moving KMeans (2D-MKM) algorithms respectively. The performances of these two proposed algorithms are compared with three of the commonly used conventional clustering algorithms, namely K-Means (KM), Fuzzy C-Means (FCM), and M...

Clustering task aims at the unsupervised classification of patterns in different groups. To enhance the quality of results, the emerging swarm-based algorithms now-a-days become an alternative to the conventional clustering methods. In this study, an optimization method based on the swarm intelligence algorithm is proposed for the purpose of clustering. The significance of the proposed algorith...

Clustering is a predominant technique used in image segmentation due to its simple, easy and efficient approach. It very important for the analysis, extraction interpretation of images; which makes it multiple applications various fields. In this article, we propose different based on cooperation between an optimization algorithm Cuckoo Search Algorithm (CSA) clustering Fuzzy C-means (FCM). The...

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...

In this paper an optimized fuzzy logic based segmentation for abnormal MRI brain images analysis is presented. A conventional fuzzy c-means (FCM) technique does not use the spatial information in the image. In this research, we use a FCM algorithm that incorporates spatial information into the membership function for clustering. The FCM algorithm that incorporates spatial information into the m...

Despite its potential advantages for fMRI analysis, fuzzy C-means (FCM) clustering suffers from limitations such as the need for a priori knowledge of the number of clusters, and unknown statistical significance and instability of the results. We propose a randomizationbased method to control the false positive rate and estimate statistical significance of the FCM results. Using this novel appr...

Some of the well-known fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. Gustafson-Kessel clustering algorithm and Gath-Geva clustering algorithm were developed to detect non-spherical structural clusters. However, the former needs added constraint of fuzzy covariance matrix, the later can only be used for the d...

0957-4174/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.eswa.2010.07.112 ⇑ Corresponding author. E-mail addresses: [email protected] (H. I org (A. Abraham). Fuzzy clustering is an important problem which is the subject of active research in several real-world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient,...