Pattern Recognition: 7th Mexican Conference, MCPR 2015, by Jesús Ariel Carrasco-Ochoa, José Francisco

By Jesús Ariel Carrasco-Ochoa, José Francisco Martí­nez-Trinidad, Juan Humberto Sossa-Azuela, José Arturo Olvera López, Fazel Famili

This ebook constitutes the refereed court cases of the seventh Mexican convention on development reputation, MCPR 2015, held in Mexico urban Mexico, in June 2015.

The 30 revised complete papers awarded have been conscientiously reviewed and chosen from sixty three submissions. The papers are prepared in topical sections on trend attractiveness and synthetic intelligence; photo processing and research; robotics and machine imaginative and prescient; average language processing and popularity; and functions of development recognition.

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IEEE (1990) 12. : Improved heuristics for optimal pathfinding on game maps. In: American Association for Artificial Intelligence (AIIDE). pp. 9–14 (2006) 13. : Simple object recognition based on spatial relations and visual features represented using irregular pyramids. Multimedia Tools Appl. 63(3), 875–897 (2013) 14. : Simulating multigraph transformations using simple graphs. In: Proceedings of the Sixth International Workshop on Graph Transformation and Visual Modeling Techniques, Braga, Portugal.

Knowl. Inf. Syst. 29(2), 249–272 (2011) 11. : A density-based clustering over evolving heterogeneous data stream. In: Proceeding of the 2nd International Colloquium on Computing, Communication, Control, and Management, pp. 275–277 (2009) 12. , Palpanas, T. : Density-based projected clustering over high dimensional data streams. In: Proceedings of the 12th SIAM International Conference on Data Mining, pp. 987–998 (2012) 13. : A single pass algorithm for clustering evolving data streams based on swarm intelligence.

By using the distance from the farthest objects to the center, CF2 can be replaced by the farthest object, the farthest object will be called xf, also the timestamp Tf must be stored, since over time the relevance of the object also affects the radius. A. Aroche-Villarruel et al. where δt = t − Tf is the difference between the current time and the time in which the farthest object xf arrived; ci is the i-th component of the micro-cluster center and, xfi is the i-th component of the object xf. Using the proposed expression (4) allows to get more micro-clusters and they are less separated, which helps in the final phase to obtain a better clustering.

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