“Flame”: A Fuzzy Clustering Method to Detection Prototype in Socio- Economic Context

Abstract

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 [1] 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.

The contribution is the result of joint reflections by the authors, with the following contributions attributed to S. Montrone (chapters 2), to P. Perchinunno (chapter 1 and paragraph 4.2), and to S. L’Abbate (chapter 3 and paragraph 4.1). The conclusions are the result of the common considerations of the authors.