In this paper, we propose an algorithm to compute initial cluster centers for K-means clustering. Data in a cell is partitioned using a cutting plane that divides cell in two smaller cells. The plane is perpendicular to the data axis with the highest variance and is designed to reduce the sum squared errors of the two cells as much as possible, while at the same time keep the two cells far apart as possible. Cells are partitioned one at a time until the number of cells equals to the predefined number of clusters, K. The centers of the K cells become the initial cluster centers for K-means. The experimental results suggest that the proposed algorithm is effective, converge to better clustering results than those of the random initialization ...
Traditional K-means algorithm's clustering effect is affected by the initial cluster center poin...
K-means clustering is a very popular clustering technique, which is used in numerous applications. ...
K-means is one of the simplest unsupervised learning algorithms that solve the well known clustering...
along the Data Axis with the Highest Variance Abstract—In this paper, we propose an algorithm to com...
Clustering is one of the widely used knowledge discovery techniques to reveal structures in a datase...
K-means clustering algorithm which is a process of separating n number of points into K clusters acc...
Abstract — Clustering is the most important unsupervised learning technique of organizing objects in...
###EgeUn###K-means clustering algorithm which is a process of separating n number of points into K c...
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects i...
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects i...
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects i...
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects i...
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects i...
In this paper, the standard k-means algorithm has been improved in terms of the initial cluster cent...
11th IEEE International Conference on Application of Information and Communication Technologies (AIC...
Traditional K-means algorithm's clustering effect is affected by the initial cluster center poin...
K-means clustering is a very popular clustering technique, which is used in numerous applications. ...
K-means is one of the simplest unsupervised learning algorithms that solve the well known clustering...
along the Data Axis with the Highest Variance Abstract—In this paper, we propose an algorithm to com...
Clustering is one of the widely used knowledge discovery techniques to reveal structures in a datase...
K-means clustering algorithm which is a process of separating n number of points into K clusters acc...
Abstract — Clustering is the most important unsupervised learning technique of organizing objects in...
###EgeUn###K-means clustering algorithm which is a process of separating n number of points into K c...
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects i...
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects i...
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects i...
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects i...
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects i...
In this paper, the standard k-means algorithm has been improved in terms of the initial cluster cent...
11th IEEE International Conference on Application of Information and Communication Technologies (AIC...
Traditional K-means algorithm's clustering effect is affected by the initial cluster center poin...
K-means clustering is a very popular clustering technique, which is used in numerous applications. ...
K-means is one of the simplest unsupervised learning algorithms that solve the well known clustering...