Customer Segmentation Based on RFM Model Using K-Means, K-Medoids, and DBSCAN Methods

Document Clustering using K-Means and K-Medoids

With the huge upsurge of information in day-to-day’s life, it has become difficult to assemble relevant information in nick of time. But people, always are in dearth of time, they need everything quick. Hence clustering was introduced to gather the relevant information in a cluster. There are several algorithms for clustering information out of which in this paper, we accomplish K-means and K-M...

Colour image segmentation using K – Medoids Clustering

K – medoids clustering is used as a tool for clustering color space based on the distance criterion. This paper presents a color image segmentation method which divides colour space into clusters. Through this paper, using various colour images, we will try to prove that K – Medoids converges to approximate the optimal solution based on this criteria theoretically as well as experimentally. Her...

K-Medoids For K-Means Seeding

We run experiments showing that algorithm clarans (Ng et al., 2005) finds better Kmedoids solutions than the standard algorithm. This finding, along with the similarity between the standard K-medoids and K-means algorithms, suggests that clarans may be an effective K-means initializer. We show that this is the case, with clarans outperforming other popular seeding algorithms on 23/23 datasets w...

Segmentation of Photospheric Solar Images by Using c-Means,k-Means, and FCM Algorithms

A K-means-like Algorithm for K-medoids Clustering

Clustering analysis is a descriptive task that seeks to identify homogeneous groups of objects based on the values of their attributes. This paper proposes a new algorithm for K-medoids clustering which runs like the K-means algorithm and tests several methods for selecting initial medoids. The proposed algorithm calculates the distance matrix once and uses it for finding new medoids at every i...