2012/12/06 by José E. Chacón, Chacón, José E.
Computer Science · #Advanced Clustering Algorithms Research #Bayesian Methods and Mixture Models #Data Management and Algorithms #Differential Geometry (math.DG) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.1212.1384
openalex publication_date 2012/12/06 · openalex created_date 2025/10/24 · openalex updated_date 2026/07/28
The problem of finding groups in data (cluster analysis) has been extensively\nstudied by researchers from the fields of Statistics and Computer Science,\namong others. However, despite its popularity it is widely recognized that the\ninvestigation of some theoretical aspects of clustering has been relatively\nsparse. One of the main reasons for this lack of theoretical results is surely\nthe fact that, unlike the situation with other statistical problems as\nregression or classification, for some of the cluster methodologies it is quite\ndifficult to specify a population goal to which the data-based clustering\nalgorithms should try to get close. This paper aims to provide some insight\ninto the theoretical foundations of the usual nonparametric approach to\nclustering, which understands clusters as regions of high density, by\npresenting an explicit formulation for the ideal population clustering.\n