Bayes Clustering Operators for Known Random Labeled Point Processes

• Autores: Dalton, Lori A.; Benalcazar Palacios, Marco Enrique; Brun, Marcel; Dougherty, Edward
• Año de publicación: 2013
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: Clustering Algorithms; Partitioning Algorithms; Couplings; Probabilistic Logic; Error Analysis; Labeling; Hamming Distance
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

There is a widespread belief that clustering is inherently subjective. To quote A. K. Jain, "As a task, clustering is subjective in nature. The same dataset may need to be partitioned differently for different purposes." One is then left with a number of questions: Where do clustering algorithms account for statistical properties of the sampling procedure? How can one address the ability of a clusterer to make inferences without a definition of its predictive capacity? This work develops a probabilistic theory of clustering that fully parallels the well-developed Bayes decision theory for classification, making it possible to address these questions and transform clustering from a subjective activity to an objective operation.
Fil: Dalton, Lori A.. Ohio State University; Estados Unidos
Fil: Benalcazar Palacios, Marco Enrique. Secretaríıa Nacional de Educación Superior; Ecuador. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; Argentina
Fil: Brun, Marcel. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; Argentina
Fil: Dougherty, Edward. Texas A&M University; Estados Unidos. Translational Genomics Research Institute. Phoenix; Estados Unidos