Fuzzy c-means (FCM) clustering algorithm is widely used for image segmentation. The purpose of clustering is to identify natural groupings of data from a large data set, which results in concise representation of system’s behavior. It can be used to detect icebergs regardless of ambient conditions like rain, darkness and fog. As a result SAR images can be used for iceberg surveillance. In this ...

Data has an important role in all aspects of human life and so analyzing this data for discovering proper knowledge is important. Data mining refers to find useful information (extracting patterns or knowledge) from large amount of data. Clustering is an important data mining technique which aims to divide the data objects into meaningful groups called as clusters. It is the process of grouping...

Fourier-transform infrared spectroscopy (FTIR) is an efficient, sensitive and computer operated technique that can detect changes in cellular composition that may reflect the onset of a disease. As such, it is being investigated as a method for automatic early detection of pre-cancerous changes. In previous work, FTIR spectral data was first empirically pre-processed and then classified using v...

In this paper, a clustering algorithm named KHarmonic means (KHM) was employed in the training of Radial Basis Function Networks (RBFNs). KHM organized the data in clusters and determined the centres of the basis function. The popular clustering algorithms, namely K-means (KM) and Fuzzy c-means (FCM), are highly dependent on the initial identification of elements that represent the cluster well...

This paper proposes a new exponential clustering algorithm (XPFCM) by reformulating the clustering objective function with an additional parameter p to adjust the exponential behavior for membership assignment. The clustering experiments show that the proposed method assign data to the clusters better than other fuzzy C-means (FCM) variants.

To overcome the shortcomings of falling into local optimal solutions and being too sensitive to initial values of the traditional fuzzy C-mean clustering algorithm, a weighted fuzzy C-means (FCM) clustering algorithm based on adaptive differential evolution (JADE) is proposed in this paper. To consider the particular contributions of different features, a ReliefF algorithm is used to assign the...

Load profiling refers to a procedure that leads to the formulation of daily load curves and consumer classes regarding the similarity of the curve shapes. This procedure incorporates a set of unsupervised machine learning algorithms. While many crisp clustering algorithms have been proposed for grouping load curves into clusters, only one soft clustering algorithm is utilized for the aforementi...

Initialization problem is a significant issue in FCM-type clustering models, in which alternative optimization is often started with random initial partitions and can be trapped into local optima caused by bad initialization. The deterministic clustering approach is a practical procedure for utilizing a robust feature of very fuzzy partitions and tries to converge the iterative FCM process to a...