A well-known clustering algorithm is K-means. This algorithm, besides advantages such as high speed and ease of employment, suffers from the problem of local optima. In order to overcome this problem, a lot of studies have been done in clustering. This paper presents a hybrid Extended Cuckoo Optimization Algorithm (ECOA) and K-means (K), which is called ECOA-K. The COA algorithm has advantages ...

The K-Means algorithm for clustering has the drawback of always maintaining K clusters. This leads to ineffective handling of noisy data and outliers. Noisy data is defined as having little similarity with the closest cluster’s centroid. In K-Means a noisy data item is placed in the most similar cluster, despite this similarity is low relative to the similarity of other data items in the same c...

k-means has recently been recognized as one of the best algorithms for clustering unsupervised data. Since k-means depends mainly on distance calculation between all data points and the centers, the time cost will be high when the size of the dataset is large (for example more than 500millions of points). We propose a two stage algorithm to reduce the time cost of distance calculation for huge ...

We consider the problem of data clustering with unidentified feature quality and when a small amount labelled is provided. An unsupervised sparse method can be employed in order to detect subgroup features necessary for semi-supervised use create constraints enhance solution. In this paper we propose K-Means variant that employs these techniques. show algorithm maintains high performance other ...