“Flame”: A Fuzzy Clustering Method to Detection Prototype in Socio- Economic Context
Cluster analysis is highly advantageous as it provides “relatively distinct” (or heterogeneous) clusters, each consisting of units (families) with a high degree of “natural association”. Different approaches to cluster analysis are characterized by the need to define a matrix of dissimilarity or distance between the n pairs of observations. The cluster analysis allows to identify the profiles families who meet certain descriptive characteristics, not defined a priori. Fuzzy clustering is useful to mine complex and multi-dimensional data sets, where the members have partial or fuzzy relations. Among the various developed techniques, fuzzy-C-means (FCM) algorithm is the most popular one, where a piece of data has partial membership with each of the pre-defined cluster centers. Fu and Medico  developed a clustering algorithm to capture dataset-specific structures at the beginning of DNA microarray analysis process, which is known as Fuzzy clustering by Local Approximation of Membership (FLAME). It worked by defining the neighborhood of each object and Identifying cluster supporting objects that have great importance in the field of market research in order to identify not only the average profiles (centroid) but the real prototypes and assigned to other units observed a degree of similarity.
KeywordsFuzzy clustering Hardship Flame Prototypes
Unable to display preview. Download preview PDF.
- 1.Fu, L., Medico, E.: FLAME: a novel fuzzy clustering method for the analysis of dna microarray data. BMC Bioinformatics 8(3), (2007). doi: 10.1186/1471-2105-8-3
- 2.Montrone, S., Perchinunno, P., L`Abbate, S., Zitolo, M.R.: The lifestyles of families through fuzzy C-means clustering. In: Murgante, B., Misra, S., Rocha, A.M.A., Torre, C., Rocha, J.G., Falcão, M.I., Taniar, D., Apduhan, B.O., Gervasi, O. (eds.) ICCSA 2014, Part III. LNCS, vol. 8581, pp. 122–134. Springer, Heidelberg (2014)Google Scholar
- 3.Fabbris, L.: Analisi esplorativa di dati multidimensionali, Cleup editore (1990)Google Scholar
- 4.Green, P.E., Frank, R.E., Robinson, P.J.: Cluster Analysis in text market selection. Management science (1967)Google Scholar
- 6.Chattopadhyay, S., Pratihar, D.K., De Sarkar, S.C.: A comparative study of fuzzy c-means algorithm and entropy-based fuzzy clustering algorithms. Computing and Informatics 30, 701–720 (2011)Google Scholar
- 7.Ma, P., Chan, K.: Incremental Fuzzy Mining of Gene Expression Data for Gene Function Prediction. IEEE Trans. on Biomedical Engineering (2010)Google Scholar
- 10.Cheli, B., Lemmi, A.A.: Totally fuzzy and relative approach to the multidimensional analysis of poverty. Economic Notes 24(1), 115–134 (1995)Google Scholar
- 11.Cerioli, A., Zani, S.: A fuzzy approach to the measurement of poverty. In: Dugum, C., Zenga, M. (eds.) Income and Wealth Distribution, Inequality and Poverty, pp. 272–284. Springer Verlag, Berlin (1980)Google Scholar