Segmentation of Tourist Interest on Tourism Object Categories by Comparing PSO K-Means and DBSCAN Method (original) (raw)

Application of K-Means Algorithm in Grouping of City Tourism City Pagar Alam

Sinkron, 2022

The purpose of this study was to analyze the application of the k-means algorithm in classifying tourist visits to the city of Pagar Alam. The k-means algorithm in grouping tourist objects begins by determining the number of clusters to be formed, determining the centroid value of each cluster, calculating the distance between the data, and calculating the minimum object distance calculated. There are 10 tourism objects that are superior from the data from the Tourism Office of the City of Pagar Alam. The research data used is the number of tourist visitors during the COVID-19 pandemic, namely 2020. The data are grouped into 4 clusters, namely C1 = high number of tourist visitors, C2 = moderate number of tourist visitors, C3 = low number of tourist visitors, C4 = number of visitors travel is very low. the centroid values ​​used are C1 = 92,494, Centroid C2 = 71,658, Centroid C3 = 26,981 and centroid C4 = 4,485. then we get the results of grouping C1=Green Paradise tourism, C2=Janang...

Bagged clustering and its application to tourism market segmentation

Expert System with applications, 2013

Dissimilarity measures for quantitative and qualitative data Tourism market segmentation Normalized weighted Shannon entropy a b s t r a c t 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 replacem ent are generated by drawing from the original sample. The fuzzy C-medoids Clustering (FCMdC) method is run on each bootstrap sample, obtaining (B Â 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 Â C) medoids. The best partition of the medoids is obtained investigating properly the dendrogram. Then each unit is assigne d 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.

The Optimization of the Dynamic K-Means Clustering Algorithm with the Cluster Initialization in Grouping Travelers Perception to the Beach Tourist Destinations in Bali, Indonesia

2017

Segmentation is the first process in determining positioning. Segmentation of tourist destinations is done by taking into account the perception of tourists to the attribute of tourist destinations that include the conditions of nature and outdoor activities, music and nightlife, arts and culture, food and drinks, social conditions, cost of living, education, health, property, and environmental stewardship life. Segmentation uses clustering methods by applying dynamic clustering algorithm on the k-means. The use of the dynamic k-means clustering algorithms could work more optimally combined with the initialization of the cluster using the mean algorithm based on k-means on the initial stage. It can be seen from the variance, SSE, DBI obtain the smallest value and the closest PC to 1 as compared with the traditional k-means algorithm and dynamic k-means clustering algorithm. The resulting cluster of k-means clustering algorithm dynamically with the initialization of cluster consists ...

A combination of algorithm agglomerative hierarchical cluster (AHC) and K-means for clustering tourism in Madura-Indonesia

Journal of Mathematical and Computational Science, 2022

The development approach through the tourism sector is one of the programs launched by the government since 2016. However, the development approach is not carried out in all areas because the number of accommodation and public facilities is minimal and uneven, one of which is in Madura. With so many tourist objects in Madura, it is necessary to distribute the development of public facilities and analyze tourism that has a non-strategic distance to public facilities to help increase tourist visits. This study builds a system for clustering tourist attractions in each district in Madura based on the distance to public facilities which include hotels, gas stations, restaurants, and mosques which are important criteria and considerations for tourists in visiting a tourist location. The method used in this research is a combination of the AHC method with K-Means. The test results of the AHC, K-Means method, and the combination of AHC and K-Means methods using the Silhouette Coefficient m...

PERSONALIZED TOURISM RECOMMENDATIONS FOR VISITORS TO GEORGIA USING HYBRID CLUSTERING

INTERNATIONAL CONFERENCE ON GLOBAL PRACTICE OF MULTIDISCIPLINARY SCIENTIFIC STUDIES-IV, 2023

Personalized tourism recommendations have become increasingly popular in recent years, with the aim of improving the visitor experience and enhancing the competitiveness of the tourism industry. In this study, we propose a hybrid clustering approach for generating personalized tourism recommendations in Georgia. The approach combines k-means clustering and hierarchical clustering techniques to identify groups of visitors with similar interests and preferences. We collected data from multiple sources, including social media data, review data, and user profiles, and used natural language processing techniques to extract visitor preferences and opinions. We then applied the hybrid clustering approach to the data to generate personalized recommendations for each visitor. Our results show that the hybrid clustering approach outperformed other clustering techniques in terms of clustering quality and diversity of the clusters. The personalized recommendations generated by the approach were found to be more relevant and useful to visitors than those generated by other techniques. We also identified the strengths and weaknesses of the hybrid approach and suggested potential improvements. The main strength of the approach is its ability to incorporate multiple sources of data and generate personalized recommendations that reflect the preferences and opinions of visitors. However, the approach may be limited by the quality and quantity of the data available. Our study has several implications for the tourism industry in Georgia. The personalized recommendations generated by the hybrid clustering approach can help tourism service providers to tailor their offerings to the specific needs and interests of visitors, thereby enhancing their satisfaction and loyalty. The approach can also help to attract new visitors by providing them with relevant and useful information about the destination.Overall, our study demonstrates the potential of hybrid clustering approaches for generating personalized tourism recommendations, and highlights the need for further research in this area to improve the quality and effectiveness of these recommendations.

Development and evaluation of different models for predicting tourist category from texts

2017

József Balogh: On some geometric applications of the container method Béla Csaba: Embedding graphs having Ore-degree at most five Dezső Miklós: On the vertex and edge sign balances of (hyper)graphs István Miklós: The swap Markov chain is rapidly mixing on the realizations of linearly bounded degree sequences Coffee break SESSION 11.00-12.30 Chair: Andrej Brodnik Rolf Niedermeier: Fixed-parameter tractability inside P Benedek Nagy: On 5'-3' Watson-Crick finite and pushdown automata Moritz von Looz: Parallel mesh (re)partitioning with balanced k-means Sándor Szabó: Cliques and differential equations Lunch SESSION 14.00-16.00 Chair: József Békési Nysret Musliu: Improving the efficiency of dynamic programming on tree decompositions via machine learning Branko Kavšek: Development and evaluation of different models for predicting tourist category from texts Csaba Raduly-Baka: Discrete structures in access road design

Developing a group urban tourism recommendation system based on the modified k-means algorithm and fuzzy best-worst method

Group recommendation is among the major concerns in urban tour guiding systems. The main challenge is the uncertainty of users’ opinions in conjunction with their preferences, which ultimately leads to the recommendation of unsuitable locations. As the number of unsuitable points of interest (POIs) for each person (tourist) increases, the efficiency of the tour guiding system faces a major decline. This paper seeks to model the uncertainty of urban tourists’ opinions regarding POIs by introducing a two-stage approach called ‘first-clustering, second recommending. The main contributions are the clustering of users based on their attributes via a modified k-means algorithm and the management of opinions using the fuzzy best-worst method (F-BWM). The proposed method is programmed for mobile applications under the name ‘G-tourism’. 485 different users registered in the mobile application and completed all the application wizard pages and 12 tours have been recognized. For each group, th...

Clustering Hostels Data for Customer Preferences using K-Prototype Algorithm

International Journal of Emerging Trends in Engineering Research, 2020

Reviews on hostel booking platforms can be used to collect rating data from customers which can be useful for service providers and customers in the future to determine hostel choices based on their individual preferences. The rating data can be used to determine the pattern of hostel selection by customers and can provide suggestions according to customer preferences in choosing hostels in certain areas. In this study, the data will be grouped using the k-prototype algorithm which is a combination of k-means and k-mode algorithms so that it is possible to group mixed attributes. The results of this study are to determine the data segment in accordance with user behavior in selecting the hostel at the time, location of the hostel and certain conditions, so that by knowing the segmentation profile, the hostel service provider can easily provide product promotions to segmented customers.

Tourism Segmentation by Consumer-Based Variables

Routledge Advances in Tourism, 2008

The basis for successful marketing is to understand and satisfy consumer needs. Sometimes it is even possible to satisfy one individual customer's needs. In the tourism industry an individually customized tourism experience can be developed, but the market for such high-end tourism products is small. This does not, however, mean that the only alternative is to appeal to the mass market. The intermediate solution is to understand which groups of tourists have similar needs and develop tourism products that match group needs. This approach is referred to as market segmentation. The aim of this chapter is to analyze market segmentation studies in tourism research over the past decade, review recent prototypical examples of different segmentation approaches and discuss theoretical and methodological issues related to market segmentation studies. Recommendations are presented that provide guidance to researchers and students with respect to how to best avoid potential pitfalls that may lead to misinterpretations of segmentation solutions and, consequently, sub-optimal strategic decisions.

Mokry, S., Dufek, O. - Q Method and its Use for Segmentation in Tourism

This paper discusses the use of Q method as a useful tool for market segmentation in tourism. Q method is classified as exploratory inductive empirical research method. For the realization of this research the web application running within the server umbrela.mendelu.cz was used. The investigation was carried out using photographic materials. This online application was created at FBE Mendel University in Brno and implements Q method to online environment using visual information. The paper comprises description of the methodology of the Q method. The aim of the research was to identify groups of users of tourism products based on their preferences in relation to the preferred type of destinations. Photographs used for research depicted various types of destinations available in the Czech Republic have been selected on the basis of a content analysis of photographic database available at CzechTourism website. Total of 63 respondents from the students of Mendel University in Brno participated in this research. According to the survey results user segments based on their preferences in relation to the preferred destination were designed. Identification data of the respondents and other qualitative data about their preferences were obtained using the complementary questionnaire. Outcomes of this survey are included in long-term research project focused on the carrying capacity of tourism destinations and preferences of visitors in relation to tourist population in destination.