This paper presents a new growing neural network for cluster analysis and market segmentation, which optimizes the size and structure of clusters by iteratively checking them for multivariate normality. We combine the recently published SGNN approach [8] with the basic principle underlying the Gaussian-means algorithm [13] and the Mardia test for multivariate normality [18, 19]. The new approac...

traditional leveraging statistical methods for analyzing today’s large volumes of spatial data have high computational burdens. to eliminate the deficiency, relatively modern data mining techniques have been recently applied in different spatial analysis tasks with the purpose of autonomous knowledge extraction from high-volume spatial data. fortunately, geospatial data is considered a proper s...

Botanical origin of the nectar predominantly affects the chemical composition of honey. Analytical techniques used for reliable honey authentication are mostly time consuming and expensive. Additionally, they cannot provide 100% efficiency in accurate authentication. Therefore, alternatives for the determination of floral origin of honey need to be developed. This study aims to discriminate cha...

This paper presents a new growing neural network for cluster analysis and market segmentation, which optimizes the size and structure of clusters by iteratively checking them for multivariate normality. We combine the recently published SGNN approach [8] with the basic principle underlying the Gaussian-means algorithm [13] and the Mardia test for multivariate normality [18, 19]. The new approac...

background and aims: pelvic organ prolapse (pop) is a common condition which affects on a large proportion of women. the objective of this study was to determine the risk factors of pop in iranian women. methods: this cross-sectional study was carried out to examine the role of demographic, anthropometric and clinical characteristics in pop disease in a sample of 365 females in ilam, iran. exam...

In this paper we propose a new algorithm to perform clustering of multivariate and functional data. We study the case of two populations different in their covariances, rather than in their means. The algorithm relies on a proper quantification of distance between the estimated covariance operators of the populations, and subdivides data in two groups maximising the distance between their induc...

Page 25 Abstract: This review paper discusses about part of multivariate data clustering techniques and their subparts. Factor analysis is a type of multivariate statistical approach commonly used in psychology, education, and more recently in the health-related professions. Factor analysis is an important tool that can be used in the development, refinement, and evaluation of tests, scales. Th...