This paper proposes the first model-based clustering algorithm for multivariate functional data. After introducing multivariate functional principal components analysis (MFPCA), a parametric mixture model, based on the assumption of normality of the principal components, is defined and estimated by an EM-like algorithm. The main advantage of the proposed model is its ability to take into accoun...

relationships between soil factors and vegetation types in playa of damghan was investigated. six vegetationtypes including artemisia.sieberi, arundo. sp, artemisia.sieberi-peganum.harmala, alhagi.cammelerom,artemisia.sieberi-petropyron.sp, tamarix.passerinoides-halocnemum strobilaceum. one bare land was recognized inthe study area and the research was conducted within mentioned types. canopy p...

the aim of this study was to investigate relationships between soil properties and plant species to determine the most effective factors separating vegetation communities in rineh rangeland. three stratifying variables were selected including slop, aspect and elevation. the study area was partitioned by combining these classes to generate homogenous units. 1m2 quadrates were located at sampling...

There is significant literature which explores methods for clustering timeseries gene-expression data sets, such as the classical data set due to Spellman et al. (1998). For instance James and Hastie (2001) use linear or quadratic discriminant functions on fitted curves, while Bar-Joseph et al. (2003) using a similar approach, do the clustering based on the coefficients of the fitted splines. I...

Traditional multivariate clustering approaches are common in many geovisualization applications. These algorithms are used to define geodemographic profiles, ecosystems and various other land use patterns that are based on multivariate measures. Cluster labels are then projected onto a choropleth map to enable analysts to explore spatial dependencies and heterogeneity within the multivariate at...

This paper considers a clustering method motivated by a multivariate analysis of variance model and computationally based on eigenanalysis (thus the term “spectral” in the title). Our focus is on large problems, and we present the method in the context of clustering genes using microarray expression data. We provide an e5cient computational algorithm and discuss its properties and interpretatio...

q-mode hierarchical cluster (hca) and principal component analysis (pca) were simultaneously applied to groundwater hydrochemical data from the three times in 2004: june, september, and december, along the ain azel aquifer, algeria, to extract principal factors corresponding to the different sources of variation in the hydrochemistry, with the objective of defining the main controls on the h...

The grade of membership (GoM) model uses fuzzy sets as memberships of each individual to extreme profiles (or classes) on the likelihood function of multivariate multinomial distributions. The GoM clustering algorithm derived from the GoM model is used in cluster analysis for categorical data, but it is iterated with complicated calculations. In this paper we create another approach, termed a f...