Pattern Recognition: 6th Mexican Conference, MCPR 2014, by Jose Francisco Martinez-Trinidad, Jesús Ariel

By Jose Francisco Martinez-Trinidad, Jesús Ariel Carrasco-Ochoa, Jose Arturo Olvera-López, Joaquín Salas-Rodríguez, Ching Y. Suen

This e-book constitutes the refereed complaints of the sixth Mexican convention on trend reputation, MCPR 2014, held in Cancun, Mexico, in June 2014. The 39 revised complete papers provided have been conscientiously reviewed and chosen from sixty eight submissions and are equipped in topical sections on development acceptance and synthetic intelligence; desktop imaginative and prescient; photo processing and research; animal biometric reputation and purposes of development recognition.

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Extra info for Pattern Recognition: 6th Mexican Conference, MCPR 2014, Cancun, Mexico, June 25-28, 2014. Proceedings

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Kluwer Academic Publishers, Dordrecht (1991) 9. : The discernibility matrices and functions in information systems. In: Slowi´ nski, R. ) Intelligent Decision Support, Handbook of Applications and Advances of the Rough Sets Theory, System Theory, Knowledge Engineering and Problem Solving, vol. 11, pp. 331–362. : Approximate entropy reducts. Fundamenta Informaticae 53(3-4), 365– 10. Sl¸ 390 (2002) 11. : Data analysis based on discernibility and indiscernibility. Information Sciences 177(22), 4959–4976 (2007) A Family of Two-Dimensional Benchmark Data Sets and Its Application to Comparing Different Cluster Validation Indices Jorge M.

Cluster validation indices are typically used to determine the optimal number of clusters for a particular clustering method. There are few two-dimensional benchmark data sets for clustering: in most cases such 2-D benchmark data are limited to two or more partially overlapping Gaussian blobs. An example of a set of 2-D data sets with a small set of different clustering situations can be found in [1]. In addition, there are some data sets for image segmentation with clustering solutions proposed by humans [2,3].

In fact 1NN applied to the raw data can be better than non-parameteric MMC most of the time. In this study we fixed α for 2PWMV and varied only β. If we cross-validated α we could potentially obtain lower error but at the cost of increased running time. In the current experiments 2PWMV+1NN and WMMC+1NN are the slowest methods yet still tractable for large datasets. We chose 1NN as the classification method for this study due to its simplicity and its popularity with dimensionality reduction programs.

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