Data present in abundance increases the complexity of handling them, which affects the
effective decision-making process. Hence, data clustering gains remarkable importance in …

In real-world scenarios, identifying the optimal number of clusters in a dataset is a difficult
task due to insufficient knowledge. Therefore, the indispensability of sophisticated automatic …

To better collect data in context to balance energy consumption, wireless sensor networks
(WSN) need to be divided into clusters. The division of clusters makes the network become a …

Visual inspection and assessment of the condition of metal structures are essential for
safety. Pulse thermography produces visible infrared images, which have been widely …

To better solve the complex non-linear problem between the subjective sound quality
evaluation results and objective psychoacoustics parameters, a method for the prediction of …

The field of data exploration relies heavily on clustering techniques to organize vast datasets
into meaningful subgroups, offering valuable insights across various domains. Traditional …

Data mining or Knowledge mining is researcher's selective area in current trend. Clustering
amongst which is retained as most challenging issue. In this paper, a study is conducted on …

Case adaptation is a challenging and crucial process of Case-Based Reasoning (CBR) for
power grid wind disaster emergencies. The statistical adaptation method is a traditional …

The K-means method is one of the most widely used clustering methods and has been
implemented in many fields of science and technology. One of the major problems of the k …

Clustering is the technique used to partition the homogenous data, where the data are
grouped together. In order to improve the clustering accuracy, the adaptive dynamic …