Prediction of Course Selection in E-Learning System Using Combined Approach of Unsupervised Learning Algorithm and Association Rule

Aher, Sunita B.; Lobo, L. M. R. J.

doi:10.1007/978-3-642-35864-7_22

Cited by 1 publication

(1 citation statement)

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“…Data processing was undertaken with Weka [Waikato Environment for Knowledge Analysis] software. We used this programme since it supports several standard data mining tasks including data pre-processing, clustering, classification, regression, visualisation, and feature selection [Aher and Lobo 2013]. The posterior mode was chosen and the division into groups was determined after data analysis.…”

Section: Methodsmentioning

confidence: 99%

Exploring Tourist Expenditures of the Gjirokastra District in Albania: A Cluster Analysis

Boboli

Muça

2022

aspe

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Tourism expenditures are an important indicator for measuring the impact of tourism on a destination. Information taken from the micro data of tourism expenditures, together with other socio-demographic characteristics of visitors, provides important data on the tourist demand for a destination. Visitor costs in combination with other variables can be used to profile the demand for tourist goods and services in the destination area. In this paper, the daily expenses of visitors in the Gjirokastra district of Albania are analysed. The Gjirokastra district is one of the more preferred regions and attracts a considerable number of domestic and foreign visitors, because it offers a diversity tourist attractions and services. For the purposes of this study, a survey of 200 visitors was conducted. The survey results were analysed with descriptive statistics via a cluster analysis. The survey provides detailed data on the daily expenses of visitors, divided into six main categories of a typical travel budget. Through the use of data mining cluster analysis, we identified and analysed the tourists’ daily expenses based on the purpose of their visit to the area of Gjirokaster.

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Section: Methodsmentioning

confidence: 99%