A major challenge in cluster analysis is that the number of data clusters mostly unknown and it must be estimated prior to clustering observed data. In real-world applications, often subject heavy tailed noise outliers which obscure true underlying structure Consequently, estimating becomes challenging. To this end, we derive a robust enumeration criterion by formulating problem as maximization...

Since density is considered as one of the most important numerical indices to explain quantitative values of a plant community by affecting on many aspects and activity of ecosystem, on the other hand, awareness about plants distribution pattern in environment is an essential preparation for measurement and study of vegetative cover, therefore, in this research determination of density and dist...

Finite mixture models are being increasingly used to model the distributions of a wide variety of random phenomena and to cluster data sets. In this paper, we focus on the use of normal mixture models to cluster data sets of continuous multivariate data. As normality based methods of estimation are not robust, we review the use of t component distributions. With the t mixture model-based approa...