K-means is a fundamental clustering algorithm widely used in both academic and industrial
applications. Its popularity can be attributed to its simplicity and efficiency. Studies show the …

Is there an optimal dimensionality reduction for k-means, revealing the prominent cluster
structure hidden in the data? We propose SUBKMEANS, which extends the classic k-means …

This paper presents Yinyang K-means, a new algorithm for K-means clustering. By
clustering the centers in the initial stage, and leveraging efficiently maintained lower and …

We study the topic of dimensionality reduction for $ k $-means clustering. Dimensionality
reduction encompasses the union of two approaches:\emph {feature selection} and\emph …

The K-means algorithm is among the most commonly used data clustering methods.
However, the regular K-means can only be applied in the input space, and it is applicable …

The choice of a proper similarity/dissimilarity measure is very important in cluster analysis for
revealing the natural grouping in a given dataset. Choosing the most appropriate measure …

Due to its simplicity and versatility, k-means remains popular since it was proposed three
decades ago. The performance of k-means has been enhanced from different perspectives …

Clustering is one of the main methods for getting insight on the underlying nature and
structure of data. The purpose of clustering is organizing a set of data into clusters, such that …

K-means is a popularly used clustering algorithm. Results of k-means clustering algorithm
are sensitive to initial centroids chosen that give different clustering results for different runs …

The k-means clustering algorithm is considered one of the most powerful and popular data
mining algorithms in the research community. However, despite its popularity, the algorithm …