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Data Mining
Major: Software Engineering
Code of subject: 7.121.01.E.016
Credits: 6.00
Department: Software
Lecturer: Dr. Science, professor Hrytsiuk Yuri Ivanovych
Semester: 2 семестр
Mode of study: денна
Завдання: The ability to effectively solve specialized tasks and practical problems of an innovative nature during professional activities related to all aspects of software development from the initial stages of specification creation to system support after commissioning.
Knowledge of modern mathematical methods and algorithms of Data Mining technology for analysis and processing of large volumes of information used in software engineering (ФКС2.1).
Learning outcomes: РНС2.1. Be able to develop methods for analysis and processing large data sets using known Data Mining tools.
Required prior and related subjects: Prerequisites: Research Methods and Tools in Software Engineering
Corequisites: Master’s Thesis Preparation and Defence
Summary of the subject: The discipline involves the study of the basic concepts of Data Mining technology, the methods, tools and applications of Data Mining are considered in detail. The description of each method is accompanied by a concrete example of its use. The concept of Web Mining is introduced. The analytical software market is analyzed, products from leading Data Mining manufacturers are described, and their capabilities are discussed. Special attention is focused on data analysis technology Data Mining, Text Mining, Visual Mining, as well as visual (Visual Mining) and text (Text Mining) data analysis, process analysis (Process Mining), analysis of Web resources (Web mining) and analysis are considered in real time (Real-Time Data Mining). A description of methods and algorithms for solving the main tasks of analysis: classification, clustering, etc. is given. The description of the idea of ??each method is supplemented by a specific example of its use.
Опис: Data Mining: application specifics. Data Mining technology and its Ukrainian market.
Practical usage of Data Mining methods and tools. Models and methods of Data Mining. Knowledge detection in datasets. Knowledge Management.
Object regression and classification. Methods of constructing rules, decision trees and mathematical functions. Prediction of time series.
Associative rules.
Basic clustering algorithms. Adaptive data clustering methods.
Visual Mining: usage problems and visualization tools. Methods of data visualization. Visualization as a way of data comprehension.
Text Mining. Extracting key concepts from a text. Text document classification. Methods of clustering text documents.
Web Mining. Methods of extracting Web-content.
Process Mining. PROM library.
Distributed data analysis. Systems of mobile agents.
Real-Time Data Mining. Recommendation machines.
Data Mining standards: CWM, CRISP, PMML and others.
Xelopes library.
Performance of genetic algorithms for processing big data.
Assessment methods and criteria: 1. Questioning in laboratory classes.
2. Control tests in laboratory classes.
3. Labs defense.
4. Examination control (written component (tests), oral component).
Критерії оцінювання результатів навчання: Current control: 45% (laboratory works – 30% (6 labs 5% each), interims tests on classes 15% (2 tests – 5% + 10%))
Exam: 55% (written part 50%, spoken part 5%)
Each laboratory work takes two weeks. If its defence is delayed, a student loses one point per each week of delay.
Порядок та критерії виставляння балів та оцінок: 100–88 points – (“excellent”) is awarded for a high level of knowledge (some inaccuracies are allowed) of the educational material of the component contained in the main and additional recommended literary sources, the ability to analyze the phenomena being studied in their interrelationship and development, clearly, succinctly, logically, consistently answer the questions, the ability to apply theoretical provisions when solving practical problems; 87–71 points – (“good”) is awarded for a generally correct understanding of the educational material of the component, including calculations, reasoned answers to the questions posed, which, however, contain certain (insignificant) shortcomings, for the ability to apply theoretical provisions when solving practical tasks; 70 – 50 points – (“satisfactory”) awarded for weak knowledge of the component's educational material, inaccurate or poorly reasoned answers, with a violation of the sequence of presentation, for weak application of theoretical provisions when solving practical problems; 49-26 points - ("not certified" with the possibility of retaking the semester control) is awarded for ignorance of a significant part of the educational material of the component, significant errors in answering questions, inability to apply theoretical provisions when solving practical problems; 25-00 points - ("unsatisfactory" with mandatory re-study) is awarded for ignorance of a significant part of the educational material of the component, significant errors in answering questions, inability to navigate when solving practical problems, ignorance of the main fundamental provisions.
Recommended books: 1. ЕНМК з дисципліни "Інтелектуальний аналіз даних" сертифікат № 04498. Доступний з : https://vns.lpnu.ua/course/view.php?id=4785
2. Грицюк Ю.І. Інтелектуальний аналіз даних і процесів : навч. посібник / Ю. І. Грицюк. – Львів : Вид-во НУ "Львівська політехніка", 2018. – 440 с. (рукопис)
3. Грицюк Ю.І. Інтелектуальний аналіз даних : лаборат. практикум / Ю. І. Грицюк. – Львів : Вид-во НУ "Львівська політехніка", 2016. – 160 с. (рукопис)
4. Грицюк Ю.І. Обчислювальні методи та моделі в наукових дослідженнях : монографія / Ю.І. Грицюк. – Львів : Вид-во ЛДУ БЖД, 2014. – 288 с.
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