Livia De Giovanni

Bagged Clustering and its application to tourism market segmentation

Aim of the paper is to propose a segmentation technique based on the Bagged Clustering (BC) method. In the partitioning step of the BC method, B bootstrap samples with replacement are generated by drawing from the original sample. The fuzzy C-medoids Clustering (FCMdC) method is run on each bootstrap sample, obtaining (BxC) medoids and the membership degrees of each unit to the different clusters. The second step consists in running a hierarchical clustering algorithm on the (BxC) medoids. The best partition of the medoids is obtained investigating properly the dendrogram. Then each unit is assigned to each cluster based on the membership degrees observed in the partitioning step. The effectiveness of the suggested procedure has been shown analyzing a suggestive tourism segmentation problem. We analyze two sample of tourists, each one attending a different cultural attraction, enlightening differences among clusters in socio-economic characteristics and in the motivational reasons behind visit behavior.

Midpoint radius self-organizing maps for interval-valued data with telecommunications application

The aim of this paper is to cluster units (objects) described by interval-valued information by adopting an unsupervised neural network approach. By considering a suitable distance measure for interval data, self-organizing maps to deal with interval-valued data are suggested. The technique, called midpoint radius self-organizing maps (MR-SOMs), recovers the underlying structure of interval-valued data by using both the midpoints (or centers) and the radii (a measure of the interval width) information. In order to show how the method MR-SOMs works a suggestive application on telecommunication market segmentation is described.

Temporal self-organizing maps for telecommunications market segmentation

A method for clustering time-varying data by using neural networks, i.e. Kohonen self-organizing maps (SOMs), is suggested. Some dissimilarity measures for capturing the temporal structure of the data are introduced and used in Kohonen SOMs allowing clustering of temporal data. Another method for clustering time-varying data, called dynamic tandem analysis (DTA), based on the sequential utilization of dynamic factor analysis and cluster analysis, is also considered. The methods are applied to telecommunications market segmentation on real data. The obtained results are compared and discussed.

Trimmed fuzzy clustering for interval-valued data

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

Exponential distance-based fuzzy clustering for interval-valued data

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GARCH-based robust clustering of time series

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.

ON THE MATHEMATICAL TREATMENT OF SELF-ORGANIZATION: EXTENSION OF SOME CLASSICAL RESULTS

Following a nonparametric approach, we suggest a time‐series clustering method. Our clustering approach combines the benefits connected to the interpretative power of the nonparametric representation of the time series, and the clustering and vector quantization informational gain produced by the adopted unsupervised neural networks technique, enhanced with the self‐organizing maps ordering and topological preservation abilities. The proposed clustering method takes into account a composite wavelet‐based information of the multivariate time series by adding to the information connected to the wavelet variance, namely the influence of variability of individual univariate components of the multivariate time series across scales, the information associated to wavelet correlation, represented by the interaction between pairs of univariate components of the multivariate time series at each scale, and then suitably tuning the combination of these pieces of information. In order to assess the effectiveness of the proposed clustering approach, a simulation study and an empirical application are shown. Copyright

Self-Organizing Maps for imprecise data

In several real life and research situations data are collected in the form of intervals, the so called interval-valued data. In this paper a fuzzy clustering method to analyse interval-valued data is presented. In particular, we address the problem of interval-valued data corrupted by outliers and noise. In order to cope with the presence of outliers we propose to employ a robust metric based on the exponential distance in the framework of the Fuzzy C-medoids clustering mode, the Fuzzy C-medoids clustering model for interval-valued data with exponential distance. The exponential distance assigns small weights to outliers and larger weights to those points that are more compact in the data set, thus neutralizing the effect of the presence of anomalous interval-valued data. Simulation results pertaining to the behaviour of the proposed approach as well as two empirical applications are provided in order to illustrate the practical usefulness of the proposed method.