A model-based clustering algorithm is proposed for a general class of functional data which the components could be curves or images. The random realizations measured with errors at discrete, and possibly random, points in definition domain. idea to build set binary trees by recursive splitting observations. number groups are determined data-driven way. new provides easily interpretable results...

This article considers the problem of accounting for unknown multivariate mixture distributions within Bayesian hierarchical models motivated by functional data analysis. Most nonparametric Bayes methods rely on global partitioning, with subjects assigned to a single cluster index for all their random effects. We propose a multivariate kernel partition process (KPP) that instead allows the clus...

Clustering data streams is an important task in data mining research. Recently, some algorithms have been proposed to cluster data streams as a whole, but just few of them deal with multivariate data streams. Even so, these algorithms merely aggregate the attributes without touching upon the correlation among them. In order to overcome this issue, we propose a new framework to cluster multivari...

Background and objectives: Chromatography fingerprinting is considered as a comprehensive method for quality control, diagnosis and the nature of herbal drugs, and it is important to classify the different samples of medicinal plants and determine the chemical species present in them. Methods: In this research, a new strategy based on the combination of multiva...

In proportion to the rapid development of information technology, time series are today accumulated in finance, medicine, industry and so forth. Therefore, an analysis of them is an urgent need for these applications. As solving these problems clustering time series has much been paid attention. The similarity for the clustering is commonly measured with Euclidean distance and dynamic time warp...