Analysis of spatial point patterns using hierarchical clustering algorithms

Multitype spatial point patterns with hierarchical interactions.

Multitype spatial point patterns with hierarchical interactions are considered. Here hierarchical interaction means directionality: points on a higher level of hierarchy affect the locations of points on the lower levels, but not vice versa. Such relations are common, for example, in ecological communities. Interacting point patterns are often modeled by Gibbs processes with pairwise interactio...

Spatial Clustering Using Hierarchical SOM

analysis of spatial point patterns by kernel identification

in the analysis of spatial point patterns, complete spatial randomness (csr) hypothesis,which is a restriction of a homogenous poisson process to study region a, operates as a dividinghypothesis between “regular” and “aggregated” patterns. meanwhile, many alternatives to csr inaggregated patterns are extensions of homogenous poisson processes themselves. therefore, when thecsr hypothesis is rej...

Author Clustering using Hierarchical Clustering Analysis

This paper presents our approach to the Author Clustering task at PAN 2017. We performed a hierarchical clustering analysis of different document features: typed and untyped character n-grams, and word n-grams. We experimented with two feature representation methods, log-entropy model, and tf-idf; while tuning minimum frequency threshold values to reduce the dimensionality. Our system was ranke...

Clustering of Activity Patterns Using Genetic Algorithms

Finding groups of individuals with similar activity patterns (a sequence of activities within a given time period, usually 24 hours) has become an important issue in models of activity-based approaches to travel demand analysis. This knowledge is critical to many activity-based models, and it aids our understanding of activity/travel behavior. This paper aims to develop a methodology for the cl...