Publicação
On cluster analysis of complex and heterogeneous data
| 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 | 2015-11-19T15:50:53Z | |
| dc.date.available | 2015-11-19T15:50:53Z | |
| dc.date.issued | 2014 | |
| dc.description | Proceedings of the 3rd Stochastic Modeling Techniques and Data Analysis International Conference, 11-14 June 2014, Lisbon, Portugal | pt_PT |
| dc.description | © 2014 ISAST | pt_PT |
| 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. | pt_PT |
| 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 | pt_PT |
| dc.identifier.isbn | 978-618-81257-6-6 | |
| dc.identifier.uri | http://hdl.handle.net/10451/20530 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | ISAST - International Society for the Advancement of Science and Technology | pt_PT |
| dc.relation.publisherversion | http://www.smtda.net/images/1_A-F_SMTDA2014_Proceedings_NEW.pdf | pt_PT |
| dc.subject | Three-way data | pt_PT |
| dc.subject | Symbolic data | pt_PT |
| dc.subject | Interval data | pt_PT |
| dc.subject | Cluster analysis | pt_PT |
| dc.subject | Similarity coefficient | pt_PT |
| dc.subject | Hierarchical clustering model | pt_PT |
| dc.title | On cluster analysis of complex and heterogeneous data | pt_PT |
| dc.type | conference object | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | conferenceObject | pt_PT |