Data Mining in Tourism Industry

Major: Tourism
Code of subject: 7.242.02.E.027
Credits: 5.00
Department: Ecological Safety and Nature Protection Activity
Lecturer: O.N. Kuz, PhD, Assoc. Prof.
Semester: 2 семестр
Mode of study: денна
Learning outcomes: The student should know: basic concepts and definitions of data mining; basic methods of models construction to identify dependencies in large data arrays; modern software tools for data mining in tourism; comparison criteria for models and methods of data mining; elements of the theory of artificial neural networks; differences between conventional and intelligent systems. The student should be able: to compare models and methods of data mining; to choose a particular type of a model and data mining method at solving the practical problems of tourism industry; to use modern software tools for data mining in tourism industry; to solve the problems of data clustering, classification and analysis in tourism; to analyze the results of software tools usage for data mining while solving applied problems in the field of tourism.
Required prior and related subjects: Informatics, statistics in tourism, mathematics (prerequisites).
Summary of the subject: Introduction to Data Mining in tourism. Basic terms and main features of data mining. The main stages of data mining. Typical tasks of data mining. Preliminary data processing. The accumulation of data. Typical procedures for data processing. Normalization min /max. Standardization of data. Data mining models. Regression analysis. Approximation of functions by the least squares method. The task of data clustering. K-means algorithm for clustering. The measure of the group quality. Associative rules. The problem of data classification. Naive Bayesian classifier. The method of the nearest neighbor, the samples method, the method of k nearest neighbors. Artificial neural networks. The general model of a neuron. Function of activation. Single-layer and dual-layer neural network. Kohonen networks (self-organizing networks). The use of neural networks for clustering data. Decision trees. Classification and regression trees. The CART algorithm. C4.5 algorithm. Overview of software tools for data mining. Prospects of data mining methods and tools for the tourism industry.
Assessment methods and criteria: The current control (40%) - labs exercises, oral questioning. Final control (60%) - examination.
Recommended books: 1. Барсегян, А. А. Анализ данных и процессов: учеб. пособие / А. А. Барсегян, М. С. Куприянов, И. И. Холод, М. Д. Тесс, С. И. Елизаров. — 3-е изд., перераб. и доп. — СПб.: БХВ-Петербург, 2009. — 512 с.: ил. + CD-ROM — (Учебная литература для вузов) 2. Data mining: пошук знань в даних [Текст] : [підруч. для студентів, інженерів і фахівців у сфера інтелект. аналізу даних] / Гладун А. Я., Рогушина Ю. В. – Київ , 2016. – 451 с. 3. Чубукова И. А. Data Mining/ Чубукова И. А. - М: Бином. Лаборатория знаний, 2008. - 384 с. 4. Барсегян А. А. Методы и модели анализа данных: OLAP и Data Mining / А. А. Барсегян, М. С. Куприянов, В. В. Степаненко та ін. – 2-е изд., перераб. и доп. – СПб. : БХВ-Петербург, 2004. – 336 с. 5. Дюк В. "Data Mining" : учебный курс / В. Дюк, А. Самойленко. – СПб. : Питер, 2001. – 368 с.