Affinity Propagation (AP) clustering has been successfully used in a lot of clustering
problems. However, most of the applications deal with static data. This paper considers how …

As an important technique of data analysis, clustering plays an important role in finding the
underlying pattern structure embedded in unlabeled data. Clustering algorithms that need to …

A huge amount of digital data containing useful information, called Big Data, is generated
everyday. To mine such useful information, clustering is widely used data analysis …

Clustering time series data is of great significance since it could extract meaningful statistics
and other characteristics. Especially in biomedical engineering, outstanding clustering …

Big data with a large number of observations (samples) have posed genuine challenges for
fuzzy clustering algorithms and fuzzy C-means (FCM), in particular. In this article, we …

K-medoids clustering is among the most popular methods for cluster analysis despite its use
requiring several assumptions about the nature of the latent clusters. In this paper, we …

In this paper, we develop a distributed higher-order k-medoids clustering algorithm for
networks using hop count as the distance metric, typical examples include social networks …

The rapid growth of digital information has produced massive amounts of time series data on
rich features and most time series data are noisy and contain some outlier samples, which …

Data streams have arisen as a relevant topic during the last few years as an efficient method
for extracting knowledge from big data. In the robust layered ensemble model (RLEM) …

As an important unsupervised learning method, clustering can find the hidden structures in
data effectively. With the amount of data grows larger, clustering of large data sets is a …