Application of artificial neural network in market segmentation: A review on recent trends

Despite the significance of Artificial Neural Network (ANN) algorithm to market segmentation, there is a need of a comprehensive literature review and a classification system for it towards identification of future trend of market segmentation research. The present work is the first identifiable academic literature review of the application of neural network based techniques to segmentation. Our study has provided an academic database of literature between the periods of 2000-2010 and proposed a classification scheme for the articles. One thousands (1000) articles have been identified, and around 100 relevant selected articles have been subsequently reviewed and classified based on the major focus of each paper. Findings of this study indicated that the research area of ANN based applications are receiving most research attention and self organizing map based applications are second in position to be used in segmentation. The commonly used models for market segmentation are data mining, intelligent system etc. Our analysis furnishes a roadmap to guide future research and aid knowledge accretion and establishment pertaining to the application of ANN based techniques in market segmentation. Thus the present work will significantly contribute to both the industry and academic research in business and marketing as a sustainable valuable knowledge source of market segmentation with the future trend of ANN application in segmentation.Comment: 24 pages, 7 figures,3 Table

A hybrid recommendation approach for a tourism system

Many current e-commerce systems provide personalization when their content is shown to users. In this sense, recommender systems make personalized suggestions and provide information of items available in the system. Nowadays, there is a vast amount of methods, including data mining techniques that can be employed for personalization in recommender systems. However, these methods are still quite vulnerable to some limitations and shortcomings related to recommender environment. In order to deal with some of them, in this work we implement a recommendation methodology in a recommender system for tourism, where classification based on association is applied. Classification based on association methods, also named associative classification methods, consist of an alternative data mining technique, which combines concepts from classification and association in order to allow association rules to be employed in a prediction context. The proposed methodology was evaluated in some case studies, where we could verify that it is able to shorten limitations presented in recommender systems and to enhance recommendation quality

Multi-mode partitioning for text clustering to reduce dimensionality and noises

Co-clustering in text mining has been proposed to partition words and documents simultaneously. Although the main advantage of this approach may improve interpretation of clusters on the data, there are still few proposals on these methods; while one-way partition is even now widely utilized for information retrieval. In contrast to structured information, textual data suffer of high dimensionality and sparse matrices, so it is strictly necessary to pre-process texts for applying clustering techniques. In this paper, we propose a new procedure to reduce high dimensionality of corpora and to remove the noises from the unstructured data. We test two different processes to treat data applying two co-clustering algorithms; based on the results we present the procedure that provides the best interpretation of the data

Social Media Mining with Fuzzy Text Matching: A Knowledge Extraction on Tourism After COVID-19 Pandemic

Social media mining is an emerging technique for analyzing data to extract valuable knowledge related to various domains. However, traditional text matching techniques, such as exact matching, are not always suitable for social media data, which can contain spelling mistakes, abbreviations, and variations in the use of words. Fuzzy matching is a text matching technique that can handle such variations and identify similarities between two texts, even if there are differences in spelling or phrasing. The gap in existing research is the limited use of fuzzy matching in social media mining for tourism recovery analysis. By applying fuzzy matching to social media data related to COVID-19 and tourism recovery, this research seeks to bridge this gap and extract valuable insights related to the impact of the pandemic on tourism recovery. We manually retrieved 19,462 Twitter records and differentiated the data sources using four diver parameters to indicate data related to the impact of COVID-19 on the tourism industry, such as the economy, restrictions, government policies, and vaccination. We conducted text mining analysis on the collected 7,352 words and identified 25 highly recommended words that indicated COVID-19 recovery from a tourism perspective. We separated the four words representing the tourism perspective to perform fuzzy matching as a dataset. We then used the inbound dataset on the fuzzy matching process, with the 7,352-word data collected from the text mining process. The matching process resulted in 18 words representing COVID-19 recovery from a tourism perspective

"Applications of Intelligent Systems in Tourism: Relevant Methods"

"This article presents a literature review of Intelligent Systems applications in Tourism in different parts of the world. The review focuses on the most relevant methods in free and paid databases, in English and Spanish. Most of the works deal with methodologies based on artificial intelligence, such as expert systems, fuzzy logic, machine learning, data mining, neural networks, genetic algorithms. In the review, several characteristics of the systems have been taken into account, such as, knowledge base, actors in the operation of the system, types of tourists, usefulness or not in space and time. According to the review it was found that most of the researches are deterministic models, the most used methodology in tourism are the expert systems based on rules, observing an emerging innovation in metaheuristics, mainly genetic algorithms and intelligent systems with knowledge base based on optimization methods for route choice or optimal visit plan. It is expected that this work serves to give a general perspective on the application of intelligent systems in the area of tourism, as well as a work that consolidates background for work in this area of research.

Fuzzy Clustering Approach for Marketing Recycled Products of Tabriz Municipality Waste Management Organization

The main concern of municipalities is the realization of sustainable revenues. Organizations affiliated with municipalities should play a role in generating revenue by defining specialized tasks while assisting municipal tasks. Tabriz Municipality Waste Management Organization seeks to achieve this by defining its strategies and goals. The organization has implemented various projects to generate revenue from recycled products. Poor planning and failure to fully outsource are among the obstacles of this organization. Therefore, marketing of recycled products is an important project. Lack of careful planning in this regard, marketing costs and weakness of private sector investment projects are the most important obstacles facing the organization. This article has determined the degree of homogeneity of waste organization projects in the marketing of recycled products with a fuzzy clustering approach and according to the opinions of experts. The results show that some of the organization's projects lack value. Instead, some projects, such as the construction of a recycling town with a variety of recycled products, renewable energy recycling, and plastic recycling with a variety of products, have similar features in the product mix marketing element, and this can reduce marketing costs and Focus on such projects

Recreational use of an artificial water reservoir in case of the Piechcin Diving Base

The paper aims to present divers’ opinions about the recreational use of the water reservoir in Piechcin with regards to four complex research areas. The study was conducted at the Diving Base in Piechcin located in the Żnin district, the Kuyavian-Pomeranian Voivodeship. The statistical data (divers’ opinions) were obtained employing a direct interview method via a questionnaire. The data were analysed with statistical methods, and the results were verified by applying fuzzy logic relations. The research shows that the owner has adapted and developed a post-mining excavation for divers, allowing for the safe practicing of qualified tourism. The technical and organizational conditions were assessed in two groups of respondents, namely those who expect some changes at the Base, and those who believe that further improvements are unnecessary

CLASSIFICATION OF MAJOR SELECTION BASED ON STUDENTS EXPERTISE USING C4.5 ALGORITHM

Selection and determination of majors is something that must be done by junior high school students when they want to enter senior high school. However, it is not uncommon for students to be confused in choosing the right major based on student expertise. The problems faced by many students who take majors because they follow their friends or parents and make it difficult for students to follow the available subjects according to the chosen majors and have an impact on student achievement. In analyzing the determination of the right major based on student expertise, the C4.5 algorithm is used. The classification of the C45 algorithm will produce a decision tree that can be used in determining the right direction. The results of the confusion matrix Classification of student value data in determining majors produce an accuracy value of 95.92%, class precision/class recall in Natural Sciences Major is 97.56%, class precision/class recall in Social Sciences Major is 87.50% and classification error is 4.08%. Decision tree results show the subject variables that influenced the selection of student majors were mathematics, science, ICT, skills, and tourism, The highest gain value lies in the Pariwista subject which is the root of the decision tree that is formed. The resulting rule is a math score above 82, a minimum science value of 84.5, a minimum ICT value of 85.5, so that students are more suitable for Natural Sciences Major. Meanwhile, if the value of mathematics is less than 82, tourism is less than 90.5 and skills are less than 84.5 then the student is more suitable for Social Sciences Major. &nbsp

Mining Rehabilitation Planning, Mining Heritage Tourism, Benefitsand Contingent Valuation

This article approaches the values underpinning derelict mining rehabilitation plans, their assessment in monetary terms, and reviews the empirical studies literature on this theme. The paper correspondingly contains four main aims. The first involves putting into perspective the thematic content on the rehabilitation of derelict and depressed mining areas, transforming them into mining heritage tourism products designed to trigger sustainable regional development. The second aim, concerns defining the range of benefits and values potentially arising. The third seeks to demonstrate and discuss why and how the theoretical frameworks of Total Economic Value (TEV) and economic valuation, taken together with the contingent valuation approach, enable the monetary estimation of the range of non-market individual values, through eliciting the individual’s willingness to pay (WTP) for the rehabilitation. And the fourth objective incorporates reviewing the literature on empirical studies estimating the monetary values of mining rehabilitation plans through recourse to the Contingent Valuation (CV) approach. We proceed by demonstrating that TEV, the economic valuation concept and CV are approaches appropriate to estimating the aforementioned benefits; we defend their utility as important inputs to raising the efficiency of political decision making processes and ensure local populations actively comply and participate in the rehabilitation process. Finally, we conclude that the empirical studies hitherto applied for estimating the monetary values of mining rehabilitation and remediation through recourse to CV remain very few despite the fact that this estimation type is increasingly recognised as an important tool in decision making processes on the rehabilitation of industrial cultural heritage in general, and mining heritage in particular