The k-means algorithm is generally the most known and used clustering method. There are
various extensions of k-means to be proposed in the literature. Although it is an …

K-means is one of the most simple and popular clustering algorithms, which implemented as
a standard clustering method in most of machine learning researches. The goal of K-means …

Kmeans-type clustering aims at partitioning a data set into clusters such that the objects in a
cluster are compact and the objects in different clusters are well separated. However, most …

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 …

The k-means tries to minimize the sum of the squared Euclidean distance from the mean
(SSEDM) of each cluster as its objective function. Although this algorithm is effective, it is too …

Applying k-Means to minimize the sum of the intra-cluster variances is the most popular
clustering approach. However, after a bad initialization, poor local optima can be easily …

Abstract k-means is traditionally viewed as an algorithm for the unsupervised clustering of a
heterogeneous population into a number of more homogeneous groups of objects …

A hierarchical initialization approach is proposed to the K-Means clustering problem. The
core of the proposed method is to treat the clustering problem as a weighted clustering …

Clustering is an unsupervised learning approach used to group similar features using
specific mathematical criteria. This mathematical criterion is known as the objective function …

The k-means algorithm is one of the most widely used methods to partition a dataset into
groups of patterns. However, most k-means methods require expensive distance …