Clustering algorithm is very important for data mining. Fuzzy c-means clustering algorithm is one of the earliest goal-function clustering algorithms, which has achieved much attention. This paper analyzes the lack of fuzzy C-means (FCM) algorithm and genetic clustering algorithm. Propose a hybrid clustering algorithm based on immune single genetic and fuzzy C-means. This algorithm uses the fuz...

Color quantization is an important operation with many applications in graphics and image processing. Most quantization methods are essentially based on data clustering algorithms. Recent studies have demonstrated the effectiveness of hard c-means (k-means) clustering algorithm in this domain. Other studies reported similar findings pertaining to the fuzzy c-means algorithm. Interestingly, none...

The K-means algorithm is a popular data-clustering algorithm. However, one of its drawbacks is the requirement for the number of clusters, K, to be specified before the algorithm is applied. This paper first reviews existing methods for selecting the number of clusters for the algorithm. Factors that affect this selection are then discussed and a new measure to assist the selection is proposed....

We consider the popular k-means problem in d-dimensional Euclidean space. Recently Friggstad, Rezapour, Salavatipour [FOCS’16] and Cohen-Addad, Klein, Mathieu [FOCS’16] showed that the standard local search algorithm yields a p1`εq-approximation in time pn ̈kq Opdq , giving the first polynomialtime approximation scheme for the problem in low-dimensional Euclidean space. While local search achiev...

The traditional clustering algorithm, K-means, is famous for its simplicity and low time complexity. However, the usability of K-means is limited by its shortcoming that the clustering result is heavily dependent on the user-defined variants, i.e., the selection of the initial centroid seeds and the number of clusters (k). A new clustering algorithm, called K-means+, is proposed to extend K-mea...

This paper focuses on the enhanced initial centroids for the K-means algorithm. The original kmeans is using the random choice of init ial seeds which is a major limitation of the orig inal K-means algorithm because it produces less reliab le result of clustering the data. The enhanced method of the k-means algorithm includes the computation of the weighted mean to improve the centroids initial...

A color image quantization algorithm based on Particle Swarm Optimization (PSO) is developed in this paper. PSO is a population-based optimization algorithm modeled after the simulation of social behavior of bird flocks and follows similar steps as evolutionary algorithms to find near-optimal solutions. The proposed algorithm randomly initializes each particle in the swarm to contain K centroid...