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This article is the first study to present a nuanced analysis of hostel visitors. Using the bagged clustering approach, a typology of hostel visitors was developed, involving four distinctive types of hostel visitors: (i) Hostel lovers, (ii) Landing point visitors, (iii) Flashpackers look-alikes, and (iv) Classical backpackers. [ABSTRACT FROM AUTHOR]

Spatial point pattern analysis usually concerns identifying features in an observation window where there is also noise. This identification traditionally begins with studying the second-order properties of the point pattern, and it may be done locally by using local second-order characteristics (LISA). Some properties of this local structure solve the problem of classification into feature and clutter points. This paper proposes an estimator for local pair correlation LISA functions, discusses some of its properties and considers a particular distance to measure dissimilarities. Two classification procedures to separate feature from clutter points are described. One of them adopts multidimensional scaling and support vector machines, and the other employs bagged clustering. Simulations demonstrate the performance of the method, and it is applied to a dataset concerning earthquakes in a seismic nest located in Colombia. [ABSTRACT FROM AUTHOR]

In recent years transfer learning has attracted much attention due to its ability to adapt a well-trained model from one domain to another. Fine-tuning is one of the most widely-used methods which exploit a small set of labeled data in the target domain for adapting the network. Including a few methods using the labeled data in the source domain, most transfer learning methods require labeled datasets, and it restricts the use of transfer learning to new domains. In this paper, we propose a fully unsupervised self-tuning algorithm for learning visual features in different domains. The proposed method updates a pre-trained model by minimizing the triplet loss function using only unlabeled data in the target domain. First, we propose the relevance measure for unlabeled data by the bagged clustering method. Then triplets of the anchor, positive, and negative data points are sampled based on the ranking violations of the relevance scores and the Euclidean distances in the embedded feature space. This fully unsupervised self-tuning algorithm improves the performance of the network significantly. We extensively evaluate the proposed algorithm using various metrics, including classification accuracy, feature analysis, and clustering quality, on five benchmark datasets in different domains. Besides, we demonstrate that applying the self-tuning method on the fine-tuned network help achieve better results. • Fully-unsupervised feature learning algorithm for neural networks. • Relevance score from the bagged clustering in random feature subspaces. • Ranking inconsistency of the relevance score and Euclidean distance for Triplet sampling. • Self-tuning neural networks from the Triplet samples. • Extensive Experiments to evaluate the self-tuning algorithm. [ABSTRACT FROM AUTHOR]

This article analyzes cruise tourism seasonality in Southern Europe, assessing the seasonal concentration levels by means of the Gini index. The additive decomposition of this index is used to evaluate the contribution of each port to the global seasonal concentration in the regions where they are located. It also allows the estimation of marginal relative effects to identify the most propitious ports for reducing seasonality within the Mediterranean regions. The analysis is complemented by estimating the seasonal patterns of each port. Given the significant heterogeneity revealed in the regions analyzed, a bootstrapped bagged clustering is applied to classify the ports into homogeneous groups according to their seasonal patterns. The techniques used form a methodological framework that serves as a control and monitoring tool for measuring seasonal concentration levels in cruise tourism, allowing for policies against seasonality to be tailored for this segment. [ABSTRACT FROM AUTHOR]

Abstract: This study examines the relationship between the motivation of wine tourists to visit cellar doors and destination image perception. A survey of tourists resulted in 676 useable questionnaires. Using a novel segmentation method, self‐organizing maps, and bagged clustering, the study identified 5 distinct motivation clusters. These clusters were different on only gender and previous visit to the wine region. Three clusters of destination image were identified using the same segmentation method. Significant relationships were found between the motivation and destination image clusters. Implications for destination marketing and managing the tourist experience at the winery cellar door are discussed. [ABSTRACT FROM AUTHOR]

Market segmentation is ubiquitous in marketing. Hierarchical and nonhierarchical methods are popular for segmenting tourism markets. These methods are not without controversy. In this study, we use bagged clustering on the push and pull factors of Western Europe to segment potential young Chinese travelers. Bagged clustering overcomes some of the limitations of hierarchical and nonhierarchical methods. A sample of 403 travelers revealed the existence of four clusters of potential visitors. The clusters were subsequently profiled on sociodemographics and travel characteristics. The findings suggest a nascent young Chinese independent travel segment that cannot be distinguished on push factors but can be differentiated on perceptions of the current independent travel infrastructure in Western Europe. Managerial implications are offered on marketing and service provision to the young Chinese outbound travel market. [ABSTRACT FROM PUBLISHER]

Segmentation has several strategic and tactical implications in marketing products and services. Despite hard clustering methods having several weaknesses, they remain widely applied in marketing studies. Alternative segmentation methods such as fuzzy methods are rarely used to understand consumer behaviour. In this study, we propose a strategy of analysis, by combining the Bagged Clustering (BC) method and the fuzzy C -means clustering method for fuzzy data (FCM-FD), i.e., the Bagged fuzzy C -means clustering method for fuzzy data (BFCM-FD). The method inherits the advantages of stability and reproducibility from BC and the flexibility from FCM-FD. The method is applied on a sample of 328 Chinese consumers revealing the existence of four segments (Admirers, Enthusiasts, Moderates, and Apathetics) of the perceived images of Western Europe as a tourist destination. The results highlight the heterogeneity in Chinese consumers’ place preferences and implications for place marketing are offered. [ABSTRACT FROM AUTHOR]

Highlights: [•] Bagged Clustering method is an alternative and effective method to clustering. [•] Bagged Clustering is suitable for different type of segmentation variables. [•] Ad-hoc survey was conducted among visitors of a cultural event. [•] Motivation (Likert) and travel spending (dummy) were used as segmentation variables. [•] Visitors are clustered according to motivations and travel spending separately. [ABSTRACT FROM AUTHOR]

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 (B × C) medoids and the membership degrees of each unit to the different clusters. The second step consists in running a hierarchical clustering algorithm on the (B × C) 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. [ABSTRACT FROM AUTHOR]

Abstract: Market segmentation comprises a wide range of measurement tools that are useful for the sake of supporting marketing and promotional policies also in the sector of cultural economics. This paper aims to contribute to the literature on segmenting cultural visitors by using the Bagged Clustering method, as an alternative and effective strategy to conduct cluster analysis when binary variables are used. The technique is a combination of hierarchical and partitioning methods and presents several advantages with respect to more standard techniques, such as k-means and LVQ. For this purpose, two ad hoc surveys were conducted between June and September 2011 in the two principal museums of the two provinces of the Trentino-South Tyrol region (Bolzano and Trento), Northern Italy: the South Tyrol Museum of Archaeology in Bolzano (ÖTZI), hosting the permanent exhibition of the “Iceman” Ötzi, and the Museum of Modern and Contemporaneous Art of Trento and Rovereto (MART). The segmentation analysis was conducted separately for the two kinds of museums in order to find similarities and differences in behaviour patterns and characteristics of visitors. The analysis identified three and two cluster segments respectively for the MART and ÖTZI visitors, where two ÖTZI clusters presented similar characteristics to two out of three MART groups. Conclusions highlight marketing and managerial implications for a better direction of the museums. [Copyright &y& Elsevier]