In this paper, we propose a novel Spatial Clustering with Obstacles Constraints (SCOC) based on Dynamic Piecewise Linear Chaotic Map and Dynamic Nonlinear Particle Swarm Optimization (PNPSO) and K-Medoids, which is called PNPKSCOC. The contrastive experiments show that PNPKSCOC is effective and has better practicalities, and it performs better than PSO K-Medoids SCOC in terms of quantization er...

Clustering plays a vital role in research area in the field of data mining. Clustering is a process of partitioning a set of data in a meaningful sub classes called clusters. It helps users to understand the natural grouping of cluster from the data set. It is unsupervised classification that means it has no predefined classes. Applications of cluster analysis are Economic Science, Document cla...

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 iterative step. To evaluate the proposed algorithm, we use some real and artificial data sets and compare with the results of other algor...

【Abstract】We describe a new approach to clustering of amino acid sequences using K-Medoids Method. This method combines K-Medoids method, Dynamic Programming and other new theories in Biology. Experiments have proved that our method can get satisfying results. We believe that the method we proposed in this paper is a powerful and flexible tool for clustering of amino acid sequences. 【Keywords】C...

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...

This paper proposes a new algorithm for K-medoids clustering which runs like the K-means algorithm and tests several methods for selecting.This paper proposes a new algorithm for K-medoids clustering which runs like the. A new Kmedoids clustering method that should be fast and efficient.

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...

Text clustering is one of the key research areas in data mining. k-medoids algorithm is a classical division algorithm, and can solve the problem of isolated points, But it often converges to local optimum. This article presents a improved social evolutionary programming(K-medoids Social Evolutionary Programming,KSEP). The algorithm is the k-medoids algorithm as the main cognitive reasoning alg...

In this paper, a rank-based K-medoids algorithm by a specific P system is proposed, which exhibits novel aspect of applying membrane computing in clustering. The traditional K-medoids clustering result suffers sensitivity to initial medoids selected randomly. To conquer the defect, we introduce the rank based on similarity between pairs of objects for the initialization. As a biological computi...

Clustering is one of the most important research areas in the field of data mining. Clustering means creating groups of objects based on their features in such a way that the objects belonging to the same groups are similar and those belonging to different groups are dissimilar. Here K Means, K Medoids are basic partition based clustering algorithms. One of the disadvantages of using these algo...