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K-means algorithm is a popular method in clustering analysis. After reviewing the traditional K-means algorithm, we proposed an improved K-means algorithm. At first we select the Euclidean distance or Manhattan distance as distance measure in our algorithm through calculating the rule of distance measure. Different initial centroids lead to different results. So the next step we will select the initial...
This paper presents a new hybrid algorithm, which is based on the concepts of the artificial bee colony (ABC) and greedy randomized adaptive search procedure (GRASP), for optimally clustering N objects into K clusters. The proposed algorithm is a two phase algorithm which combines an artificial bee colony optimization algorithm for the solution of the feature selection problem and a GRASP algorithm...
Clustering analysis has been an emerging research issue in data mining due its variety of applications. In the recent years, it has become an essential tool for gene expression analysis. Many clustering algorithms have been proposed so far. However, each algorithm has its own merits and demerits and can not work for all real situations. In this paper, we present a clustering algorithm that is inspired...
Clustering analysis is an important area of data mining. A kind of new clustering algorithm with ant colony optimization based on cluster center initialization is proposed in this paper. The new algorithm gives initialized cluster centers by different methods, then solves clustering problems by iterated method. Three methods of cluster center initialization are used in clustering algorithm with ant...
Fuzzy Kohonen clustering networks (FKCN) are well known for clustering analysis (unsupervised learning and self-organizing). This classification of FKCN algorithm is a set of iterative procedures that suffer some major problems, for example its constringency rate is not too fast for a large amount of datasets. To overcome these defects, an efficient fuzzy Kohonen network algorithm is proposed in this...
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