This paper proposed a new application of K-means clustering algorithm. Due to ease of implementation and application, K-means algorithm can be widely used. However, one of the disadvantages of clustering algorithms is that there is no balance between the clustering algorithm and its applications, and many researchers have paid less attention to clustering algorithm applications. The purpose of ...

In this text we propose a method which efficiently performs clustering of high-dimensional data. The method builds on random projection and the Kmeans algorithm. The idea is to apply K-means several times, increasing the dimensionality of the data after each convergence of K-means. We compare the proposed algorithm on four high-dimensional datasets, image, text and two synthetic, with K-means c...

Hierarchical clustering of text collections is a key problem in document management and retrieval. In partitional hierarchical clustering, which is more efficient than its agglomerative counterpart, the entire collection is split into clusters and the individual clusters are further split until a heuristically-motivated termination criterion is met. In this paper, we define the BIC-means algori...

The K-harmonic means clustering algorithm (KHM) is a new clustering method used to group data such that the sum of the harmonic averages of the distances between each entity and all cluster centroids is minimized. Because it is less sensitive to initialization than K-means (KM), many researchers have recently been attracted to studying KHM. In this study, the proposed iSSO-KHM is based on an im...

This paper investigated K-means algorithm, a well-known clustering algorithm. K-means clustering algorithms have some shortfalls and defects, and one defect is reviewed in this study. One of the disadvantages of K-means clustering algorithms is that they can produce clusters that do not always include all the correct components. It is due to the presence of the error rate during the clustering ...