One of the main problems in K-means clustering is setting of initial centroids which can
cause misclustering of patterns which affects clustering accuracy. Recently, a density and …

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 …

Cluster Analysis is one of the most important data mining techniques which help the
researchers to analyze the data and categorize the attributes of data into various groups. K …

Clustering is a data mining method to group data based on its features or attributes. One
reasonably popular clustering algorithm is K-Means. K-Means algorithm is often optimized …

In data mining, clustering is a method of grouping similar points together. This grouping can
be done using partitioning or hierarchical clustering algorithms. K-means is one of the …

Data mining techniques help in business decision making and predicting behaviors and
future trends. Clustering is a data mining technique used to make groups of objects that are …

Clustering is one of the main methods of data mining. K-means algorithm is one of the most
common clustering algorithms due to its efficiency and ease of use. In many data mining …

ABSTRACT K-means clustering algorithm is one of the most popular unsupervised learning
algorithm that is broadly used to clustering the given data items. The k-means algorithm is …

Performance of K-means algorithm which depends highly on initial starting points can be
trapped in local minima and led to incorrect clustering results. The lack of K-means algorithm …

Unsupervised learning is a technique to organize the data into meaningful way having
similarity. Cluster analysis is the study of clustering techniques and algorithms which are …