CC-Crowd Clusering Algorithm (Semeion©)
Crowd Clustering (CC) is an algorithm to define the mechanic of cause-effect relationship of a set of entities (that is, sensors) in a temporal discrete process in two dimensions.
CC is a generalization for data geo-referenced in 2 dimensions of a previous algorithm, able to manage in parallel many one-dimensional temporal signals (Buscema et al, 2013).
The main aim of CC is to build up a plausible Model that makes explicit the more likelihood causation process trough which each entity at each time step influences which other entities at the next time step.
 Massimo Buscema, Pier Luigi Sacco, Enzo Grossi, and Weldon A. Lodwick, Spatiotemporal Mining: A Systematic Approach to Discrete Diffusion Models for Time and Space Extrapolation, Chapter 8, pp 231-275, in W.J. Tastle (ed.), Data Mining Applications Using Artificial Adaptive Systems, DOI 10.1007/978-1-4614-4223-3_1, Springer Science+Business Media New York 2013.