Despite its well-known shortcomings, k-means remains one of the most widely used
approaches to data clustering. Current research continues to tackle its flaws while …

The spectral clustering algorithm is extensively employed in different aspects, especially in
the field of pattern recognition. However, the efficient construction of the neighborhood …

The selection of a proper distance function is crucial for analyzing the data efficiently. To find
an appropriate distance for clustering algorithm is an unsolved problem as of now. The …

In clustering, similarity measure has been one of the major factors for discovering the natural
grouping of a given dataset by identifying hidden patterns. To determine a suitable similarity …

With the rapid development of sensors and mechanical systems, we produce an
exponentially large amount of data daily. Usually, faults are prevalent in these sensory …

An improved version of K‐means clustering algorithm that can be applied to big data
through lower processing loads with acceptable precision rates is presented here. In this …

Conformal prediction uses the degree of strangeness (nonconformity) of data instances to
determine the confidence values of new predictions. We propose an inductive conformal …

A considerable amount of research has been done since long to select an appropriate
similarity or dissimilarity measure in cluster analysis for exposing the natural grouping in an …

The sustainable utilization of cultivated land is a crucial prerequisite for ensuring food
security and achieving sustainable socioeconomic development. This study employed a …