The k-means is the most popular clustering algorithm, but, as it needs too many distance
computations, its speed is dramatically fall down against high-dimensional data. Although …

The increasing effect of Internet of Things (IoT) unlocks the massive volume of the
availability of Big Data in many fields. Generally, these Big Data may be in a non …

Existing weighted clustering algorithms often heavily rely on specific parameters.
Specifically, in addition to the number of clusters (k), several other parameters need to be …

Unveiling the intrinsic structure within high-dimensional data presents a significant
challenge, particularly when clusters manifest themselves in lower-dimensional subspaces …

To solve the problem of hidden pattern recognition and high dimensional perception of
geospatial sensor data, machine learning can build a model of the unknown relationship …

Aiming at the low efficiency and accuracy of K-means algorithm in processing massive data,
an improved K-means clustering algorithm based on dissimilarity function was proposed …

This paper introduces the $ t $-divergence, a novel divergence measure associated with the
inverse tangent function. We investigate its intriguing consistent and outlier-robust features …