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.
References
[1] 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.