Data clustering is an important step in data mining and machine learning. It is especially crucial to analyze the data structures for further procedures. Recently a new clustering algorithm known as ‘neutrosophic c-means’ (NCM) was proposed in order to alleviate the limitations of the popular fuzzy c-means (FCM) clustering algorithm by introducing a new objective function which contains two typ...

Medical image segmentation is a key step towards medical image analysis. The objective of medical image segmentation is to delineate Region Of Interests (ROI) from the images. Hybridization of nature inspired algorithms with soft computing provides accurate image segmentation results in less computation time. In this work, various algorithms for medical image segmentation which help medical pra...

Many clustering techniques have been proposed for the analysis of gene expression data obtained from microarray experiments. However, choice of suitable method(s) for a given experimental dataset is not straightforward. KFCM algorithm has been widely applied in gene expression data analysis, but it is sensitive to the class center migration. Therefore, the fuzzy kernel clustering algorithm base...

geographic information and analysis provide a wide range of data and techniques to monitor and manage natural resources. as an important case, in arid and semi-arid areas, water management is critical for both local governance and citizens. as a result, the estimation of water potential brought by snowmelt runoff and rainfalls seems to be very useful and important for these areas. hydrological ...

Fuzzy C-mean (FCM) is the most well-known and widely-used fuzzy clustering algorithm. However, one of the weaknesses of the FCM is the way it assigns membership degrees to data which is based on the distance to the cluster centers. Unfortunately, the membership degrees are determined without considering the shape and density of the clusters. In this paper, we propose an algorithm which takes th...