A clustering method associated pretopological concepts and k-means algorithm
| Autor: | N. Kabachi, T. V. Le, M. Lamure |
|---|---|
| Přispěvatelé: | SI LIRIS, Équipe gestionnaire des publications, Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2) |
| Jazyk: | angličtina |
| Rok vydání: | 2007 |
| Předmět: |
Structure (mathematical logic)
Theoretical computer science Computer science 05 social sciences k-means clustering 02 engineering and technology [INFO] Computer Science [cs] Determining the number of clusters in a data set 0502 economics and business Outlier 0202 electrical engineering electronic engineering information engineering A priori and a posteriori 020201 artificial intelligence & image processing [INFO]Computer Science [cs] Limit (mathematics) 050207 economics Cluster analysis Categorical variable |
| Zdroj: | The 12th International Conference on Applied Stochastic Models and Data Analysis (ASMDA 2007) The 12th International Conference on Applied Stochastic Models and Data Analysis (ASMDA 2007), May 2007, Chania, Crete, Greece Recent advanced in stochastic modelling & data analysis Recent advanced in stochastic modelling & data analysis, 2007 |
| Popis: | International audience; The aim of this work is to define a clustering method starting from thepretopological results related to the minimal closed subset concepts which provideus the view of relations between groups in its structure; then, we consider this resultas the pre-treatment for some classical clustering algorithms. Especially, k-meansphilosophy is observed by its remarkable benefits. Thus we propose a new clusteringmethod in two processes such as structuring process and clustering one. Thismethod allows us to: obtain a data clustering for both of categorical and numericdata - exclude the limit in determination of cluster number a priori - and attainwell-shaped clusters whose shapes are not influenced on existence of outliers. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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