Clustering is an essential tool in data mining research and applications. It is the subject of
active research in many fields of study, such as computer science, data science, statistics …

Summary 16S rRNA-targeted oligonucleotide probes for eubacteria (EUB338), ammonium-
oxidizing bacteria (Nsm156) and nitrite-oxidizing bacteria (Nb1000) were used for the rapid …

Model-Based Clustering, Classification, and Denisty Estimation Using mclust in R Model-
based clustering and classification methods provide a systematic statistical approach to …

Hands-on Machine Learning with R provides a practical and applied approach to learning
and develo** intuition into today's most popular machine learning methods. This book …

The monograph Data Clustering: Theory, Algorithms, and Applications was published in
2007. Starting with the common ground and knowledge for data clustering, the monograph …

We propose a new model for active contours to detect objects in a given image, based on
techniques of curve evolution, Mumford-Shah (1989) functional for segmentation and level …

Cluster analysis is the automated search for groups of related observations in a dataset.
Most clustering done in practice is based largely on heuristic but intuitively reasonable …

" This is a great overview of the field of model-based clustering and classification by one of
its leading developers. McNicholas provides a resource that I am certain will be used by …

We consider the problem of determining the structure of clustered data, without prior
knowledge of the number of clusters or any other information about their composition. Data …

The prominence of finite mixture modelling is greater than ever. Many important statistical
topics like clustering data, outlier treatment, or dealing with unobserved heterogeneity …