Clustering has been widely employed for extracting intrinsic groups because of its low
reliance on domain knowledge. Though several clustering techniques have been developed …

In the face of pressing challenges such as climate change and dwindling biodiversity, the
global need for optimizing natural resource management is urgent. To address this, green …

Spectral clustering is a popular unsupervised learning technique used for exploratory
analysis of complex datasets. In spectral clustering, the efficient construction of a sparse …

Ship trajectory clustering is one of the main methods of ship trajectory mining based on AIS
data. However, there exist two main problems in trajectory clustering: One is the inherent …

The minimum spanning tree clustering algorithm is known to be capable of detecting
clusters with irregular boundaries. The paper presents a novel hierarchical clustering …

The exponential growth of big data presents a significant task for the incremental clustering
problem. This paper proposes a density-based total clustering method in network space (NS …

Minimum spanning trees (MSTs) provide a convenient representation of datasets in
numerous pattern recognition activities. Moreover, they are relatively fast to compute. In this …

In recent years, graph‐based data clustering algorithms have become popular as they
perform connectivity‐based rather than centroid‐based partitioning. Methods related to …

This paper presents a comparative analysis of different optimization techniques for the K-
means algorithm in the context of big data. K-means is a widely used clustering algorithm …

Most density-based clustering algorithms have the problems of difficult parameter setting,
high time complexity, poor noise recognition, and weak clustering for datasets with uneven …