Trimmed fuzzy clustering for interval-valued data
- 581 Downloads
In this paper, following a partitioning around medoids approach, a fuzzy clustering model for interval-valued data, i.e., FCMd-ID, is introduced. Successively, for avoiding the disruptive effects of possible outlier interval-valued data in the clustering process, a robust fuzzy clustering model with a trimming rule, called Trimmed Fuzzy \(C\)-medoids for interval-valued data (TrFCMd-ID), is proposed. In order to show the good performances of the robust clustering model, a simulation study and two applications are provided.
KeywordsInterval-valued data Partitioning around medoids Fuzzy clustering Robust clustering Trimming Web advertising
Mathematics Subject Classification62H30 62G35 03E72 62A86
The authors thank the editors and the three referees for their useful comments and suggestions which helped to improve the quality and presentation of this manuscript.
- Cazes P, Chouakria A, Diday E, Schektrman Y (1997) Entension de l’analyse en composantes principales à des données de type intervalle. Revue Stat Appl 45(3):5–24Google Scholar
- Fu KS (1977) Syntactic pattern recognition, applications. Springer, New YorkGoogle Scholar
- Jeng JT, Chuang CC, Tseng CC, Juan CJ (2010) Robust interval competitive agglomeration clustering algorithm with outliers. Int J Fuzzy Syst 12(3):227–236Google Scholar
- Kamdar T, Joshi A (2000) On creating adaptive Web servers using Weblog Mining. Technical Report TR-CS-00-05, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore CountyGoogle Scholar
- Kaufman L, Rousseeuw PJ (1987) Clustering by means of medoids. In: Dodge Y (ed) Statistical data analysis based on the L1-norm and related methods. North-Holland, Amsterdam, pp 405–416Google Scholar
- Kaufman L, Rousseeuw PJ (1990) Finding groups in data: an introduction to cluster analysis. Wiley, New YorkGoogle Scholar
- Krishnapuram R, Joshi A, Yi L (1999) A fuzzy relative of the k-medoids algorithm with application to web document and snippet clustering. In: IEEE international fuzzy systems conference (FUZZIEEE99), vol 3, IEEE, Seoul, pp 1281–1286Google Scholar
- Qualman E (2012) Socialnomics: How social media transforms the way we live and do business. Wiley, New JerseyGoogle Scholar
- Webb Young J, Burgoyne B (2009) You’ve got a friend: measuring the value of brand friending on social networks. In: Market research study annual conference, Market Research StudyGoogle Scholar
- Wedel M, Kamakura WA (1998) Market segmentation: conceptual and methodological foundations. Kluwer Academic, BostonGoogle Scholar