A. Staiano, A. Ciaramella, L. D. Vinco, G. Longo, G. Raiconi, R. Tagliaferri, R. Amato, C. D. Mondo, G. Mangano, G. Miele
{"title":"Visualization, clustering and classification of multidimensional astronomical data","authors":"A. Staiano, A. Ciaramella, L. D. Vinco, G. Longo, G. Raiconi, R. Tagliaferri, R. Amato, C. D. Mondo, G. Mangano, G. Miele","doi":"10.1109/CAMP.2005.54","DOIUrl":null,"url":null,"abstract":"Due to the recent technological advances, data mining in massive data sets has evolved as a crucial research field for many if not all areas of research: from astronomy to high energy physics, to genetics etc. In this paper we discuss an implementation of the Probabilistic Principal Surfaces (PPS) which was developed within the framework of the AstroNeural collaboration. PPS are a nonlinear latent variable model which may be regarded as a complete mathematical framework to accomplish some fundamental data mining activities such as: visualization, clustering and classification of high dimensional data. The effectiveness of the proposed model is exemplified referring to a complex astronomical data set.","PeriodicalId":393875,"journal":{"name":"Seventh International Workshop on Computer Architecture for Machine Perception (CAMP'05)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2005-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Seventh International Workshop on Computer Architecture for Machine Perception (CAMP'05)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CAMP.2005.54","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Due to the recent technological advances, data mining in massive data sets has evolved as a crucial research field for many if not all areas of research: from astronomy to high energy physics, to genetics etc. In this paper we discuss an implementation of the Probabilistic Principal Surfaces (PPS) which was developed within the framework of the AstroNeural collaboration. PPS are a nonlinear latent variable model which may be regarded as a complete mathematical framework to accomplish some fundamental data mining activities such as: visualization, clustering and classification of high dimensional data. The effectiveness of the proposed model is exemplified referring to a complex astronomical data set.