On cluster analysis of complex and heterogeneous data

Bacelar-Nicolau, Helena; Nicolau, Fernando C.; Sousa, Áurea; Bacelar-Nicolau, Leonor

Publication

On cluster analysis of complex and heterogeneous data

2014Conference object

dc.contributor.author	Bacelar-Nicolau, Helena
dc.contributor.author	Nicolau, Fernando C.
dc.contributor.author	Sousa, Áurea
dc.contributor.author	Bacelar-Nicolau, Leonor
dc.date.accessioned	2015-05-05T16:29:36Z
dc.date.available	2015-05-05T16:29:36Z
dc.date.issued	2014
dc.description	3rd SMTDA Conference Proceedings, 11-14 June 2014, Lisbon Portugal.	por
dc.description.abstract	Cluster analysis or "unsupervised" classification (from "unsupervised learning", in pattern recognition literature) usually concerns a set of exploratory multivariate data analysis methods and techniques for grouping either statistical data units or variables into groups of similar elements, that is finding a clustering structure in the data. Classical clustering methods usually work with a set of objects as statistical data units described by a set of homogeneous (that is, of the same type) variables in a two-way framework. This paradigm can be extended in such way that data units may be either simple / first-order elements (e.g., objects, subjects, cases) or groups of / second-order or more elements from a population (e.g., subsets, samples, classes of a partition) and/or descriptive variables may simultaneously be of different (e.g., binary, multi-valued, histogram or interval) types. Therefore, one has a complex and/or heterogeneous data set under analysis. In that case classification will often be carried out by using a three-way or a symbolic/complex approach. The present work synthesizes previous methodological results and shows several developments mostly regarding hierarchical cluster analysis of complex data, where statistical data units are described by either a homogeneous or a heterogeneous set of variables. We will illustrate that approach on a case study issued from the statistical literature. The methodology has been applied with success in a data mining context, concerning multivariate analysis of real-life data bases from economy, management, medicine, education and social sciences.	en
dc.identifier.citation	Bacelar-Nicolau, Helena; Nicolau, Fernando C.; Sousa, Áurea; Bacelar-Nicolau, Leonor (2014). "On cluster analysis of complex and heterogeneous data". Proceedings of the 3rd Stochastic Modeling Techniques and Data Analysis International Conference (SMTDA2014), C. H. Skiadas (Eds.), 2014 ISAST, 99-108.	por
dc.identifier.isbn	978-618-81257-5-9 (Book)
dc.identifier.isbn	978-618-81257-6-6 (e-Book)
dc.identifier.uri	http://hdl.handle.net/10400.3/3431
dc.language.iso	eng	por
dc.peerreviewed	yes	por
dc.publisher	ISAST - International Society for the Advancement of Science and Technology	por
dc.relation.publisherversion	http://www.smtda.net/images/1_A-F_SMTDA2014_Proceedings_NEW.pdf	por
dc.subject	Three-way Data	en
dc.subject	Symbolic Data	en
dc.subject	Interval Data	en
dc.subject	Cluster Analysis	en
dc.subject	Similarity Coefficient	en
dc.subject	Hierarchical Clustering Model	en
dc.title	On cluster analysis of complex and heterogeneous data	en
dc.type	conference object
dspace.entity.type	Publication
oaire.citation.conferencePlace	Lisboa, Portugal	por
oaire.citation.endPage	108	por
oaire.citation.startPage	99	por
oaire.citation.title	3rd Stochastic Modeling Techniques and Data Analysis International Conference (SMTDA2014	en
rcaap.rights	openAccess	por
rcaap.type	conferenceObject	por