- Autor
- Pełka Marcin (Wrocław University of Economics, Poland)
- Tytuł
- Ensemble Approach for Clustering of Interval-Valued Symbolic Data
- Źródło
- Statistics in Transition, 2012, vol. 13, nr 2, s. 335-342, rys., tab., bibliogr. 12 poz.
- Słowa kluczowe
- Analiza skupień, Zmienne jakościowe, Analiza symulacyjna
Cluster analysis, Qualitative variables, Simulation analysis - Uwagi
- summ.
Materiały z Kongresu Statystyki Polskiej: The 100th Anniversary of the Polish Statistical Association, 2012. - Abstrakt
- Ensemble approach has been applied with a success to regression and discrimination tasks [see for example Gatnar 2008]. Nevertheless, the idea of ensemble approach, that is combining (aggregating) the results of many base models, can be applied to cluster analysis of symbolic data. The aim of the article is to present suitable ensemble clustering based on symbolic data. The empirical part of the paper presents results simulation studies (based on artificial data sets with known cluster structure) of ensemble clustering based on co-occurrence matrix for symbolic interval-valued data, compared with single clustering method. The results are compared according to corrected Rand index. (original abstract)
- Dostępne w
- Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka SGH im. Profesora Andrzeja Grodka
Biblioteka Główna Uniwersytetu Ekonomicznego w Katowicach
Biblioteka Główna Uniwersytetu Ekonomicznego w Poznaniu
Biblioteka Główna Uniwersytetu Ekonomicznego we Wrocławiu - Pełny tekst
- Pokaż
- Bibliografia
-
- BILLARD, L., DIDAY E. (2006). Symbolic Data Analysis: Conceptual Statistics and Data Mining, Wiley, Chichester.
- BOCK, H.-H., DIDAY, E. (red.) (2000). Analysis of symbolic data. Explanatory methods for extracting statistical information from complex data, Springer Verlag, Berlin- Heidelberg.
- BREIMAN, L. (1996). Bagging predictors, Machine Learning, 24(2), p. 123-140.
- DE CARVALHO, FAT., LECHEVALLIER, Y., DE MELO, F M. (2012). Partitioning hard clustering algorithms based on multiple dissimilarity matrices, Pattern Recognition, 45(1), p. 447-464.
- FERN, X.Z., BRODLEY, C.E. (2004). Solving cluster ensemble problems by bipartite graph partitioning, Proceedings of the 21st International Conference on Machine Learning, Canada.
- FRED, A.L.N., JAIN, A.K. (2005). Combining multiple clustering using evidence accumulation, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 27, p. 835-850.
- GAHEMI, R., SULAIMAN, N., IBRAHIM, H., MUSTAPHA, N. (2009). A survey: Clustering ensemble techniques [in:] Proceedings of World Academy of Science, Engineering and Technology, Vol. 38, p. 636-645.
- GATNAR, E. (2008). Podejście wielomodelowe w zagadnieniach dyskryminacji i regresji, Wydawnictwo Naukowe PWN, Warszawa.
- GATNAR, E., WALESIAK, M. (red.) (2004). Metody statystycznej analizy wielowymiarowej w badaniach marketingowych, Wydawnictwo AE, Wrocław.
- HORNIK, K. (2005). A clue for cluster ensembles, Journal of Statistical Software, 14, 65-72.
- NG, A., JORDAN, M., WEISS, Y. (2002). On spectral clustering: analysis and an algorithm, [In:] T. Dietterich, S. Becker, Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems 14, MIT Press, 849-856.
- STREHL, A., GHOSH, J. (2002). Cluster ensembles - A knowledge reuse framework for combining multiple partitions, Journal of Machine Learning Research, 3, p. 583-618.
- Cytowane przez
- ISSN
- 1234-7655
- Język
- eng
