KA215001 Data Mining

Hiroshima University Syllabus

Japanese

Academic Year 2024Year School/Graduate School School of Informatics and Data Science

Lecture Code KA215001 Subject Classification Specialized Education

Subject Name データマイニング

Subject Name
（Katakana）データマイニング

Subject Name in
English Data Mining

Instructor MORIMOTO YASUHIKO

Instructor
(Katakana) モリモト　ヤスヒコ

Campus Higashi-Hiroshima Semester/Term 3rd-Year, First Semester, 1Term

Days, Periods, and Classrooms (1T) Thur1-4：ENG 103

Lesson Style Lecture Lesson Style
(More Details) 　

Lecture and related exercises by using a computer
【Online/ondemand style class by using Moodle and Teams】

Credits 2.0 Class Hours/Week 　 Language of Instruction B : Japanese／English

Course Level 2 : Undergraduate Low-Intermediate

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 02 : Information Science

Eligible Students Senior Level

Keywords Knowledge Discovery, Information Retrieval, Discovery Science, Large-scale data processing, Big Data, RESAS (Regional Economy Society Analyzing System)

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) Integrated Arts and Sciences
（Knowledge and Understanding）
・Knowledge and understanding of the importance and characteristics of each discipline and basic theoretical framework.
（Abilities and Skills）
・The ability and skills to collect and analyze necessary literature or data among various sources of information on individual academic disciplines.
・The ability and skills to specify necessary theories and methods for consideration of issues.

Computer Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.

Data Science Program
（Knowledge and Understanding）
・D1. Knowledge and ability to understand the theoretical framework of statistics and data analysis and to analyze qualitative/quantitative information of big data accurately and efficiently.
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.

Intelligence Science Program
（Knowledge and Understanding）
・D1. A deep systematic understanding of the advanced intelligence of human beings and its realization by computers.
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.

Class Objectives
/Class Outline - To understand how to retrieve necessary information from stored databases efficiently and effectively
- To understand what is valuable information and how to extract valuable information
- To understand important issues for handling large-scale data

Class Schedule 1-2: Guidance and recent data innovations
Overview of data mining technology and introduction of recent research results and case studies
3-4: Database management system
Basics of database management systems (relational algebra, SQL query language, consistency constraints, transactions, exclusive control, etc.)
5-6: Multidimensional analysis
Many facts can be understood by aggregating databases from various directions and analyzing trends. Here, you will learn and practice aggregation operations necessary for multidimensional analysis using familiar real data.
7-8: Exercise data and data mining tools
The exercise uses data from the Regional Economic Analysis System (RESAS) provided by the Ministry of Economy, Trade and Industry and the Secretariat of the Council for the Realization of the Digital Garden City National Vision, Cabinet Secretariat, and analyzes various knowledge for regional revitalization through advanced data analysis. We will excavate it through. WEKA, known as a data mining tool, is used as an analysis tool.
9-10: Association rules
Definition of association rules and association rule discovery algorithms (A priori algorithm, FP-Tree, FP-Growth, etc.)
11-12: Prediction model
Definition of discriminant rules, their optimization methods, and decision trees/regression trees
13-14: Clustering
Define data similarity and group data based on similarity
15: Data mining application
Related topics such as familiar application examples such as CRM, recommendation systems, and search engines, NoSQL databases such as key-value stores, and map reduce calculation platforms will be introduced in the 15th lecture and during gaps in the lectures.

Text/Reference
Books,etc. Reference Book:
Jiawei Han, Micheline Kamber共著「Data Mininig: Concepts and Technologies」(Morgan Kaufmann)

PC or AV used in
Class,etc. 　

(More Details) PowerPoint and Handout

Learning techniques to be incorporated 　

Suggestions on
Preparation and
Review Compute the results of sample databases by yourself and confirm the results and how they work.

Requirements

Grading Method We will ask you to submit a practice report.

Practical Experience Experienced

Summary of Practical Experience and Class Contents based on it The lecturer has been in charge of data mining research and development and product development at a corporate research institute for many years, and has a track record of introducing this technology to securities companies, hospitals, etc.

Message

Other Since this lecture is aimed at information science students who must bring Windows PC, the exercise materials were created for Windows users. If your PC is a Mac, we recommend that you enable Windows to run on your Mac in advance. (The exercise content also works on Mac. However, advanced PC knowledge is required in order to adjust Mac-specific operations. Because there are few TAs, those who are not confident that they can adjust windows exercises for Mac on their own should use Windows on Mac.)

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2024Year	School/Graduate School	School of Informatics and Data Science
Lecture Code	KA215001	Subject Classification	Specialized Education
Subject Name	データマイニング
Subject Name （Katakana）	データマイニング
Subject Name in English	Data Mining
Instructor	MORIMOTO YASUHIKO
Instructor (Katakana)	モリモト　ヤスヒコ
Campus	Higashi-Hiroshima	Semester/Term	3rd-Year, First Semester, 1Term
Days, Periods, and Classrooms	(1T) Thur1-4：ENG 103
Lesson Style	Lecture	Lesson Style (More Details)
Lecture and related exercises by using a computer 【Online/ondemand style class by using Moodle and Teams】
Credits	2.0	Class Hours/Week		Language of Instruction	B : Japanese／English
Course Level	2 : Undergraduate Low-Intermediate
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	02 : Information Science
Eligible Students	Senior Level
Keywords	Knowledge Discovery, Information Retrieval, Discovery Science, Large-scale data processing, Big Data, RESAS (Regional Economy Society Analyzing System)
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)	Integrated Arts and Sciences （Knowledge and Understanding）・Knowledge and understanding of the importance and characteristics of each discipline and basic theoretical framework. （Abilities and Skills）・The ability and skills to collect and analyze necessary literature or data among various sources of information on individual academic disciplines. ・The ability and skills to specify necessary theories and methods for consideration of issues. Computer Science Program （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. Data Science Program （Knowledge and Understanding）・D1. Knowledge and ability to understand the theoretical framework of statistics and data analysis and to analyze qualitative/quantitative information of big data accurately and efficiently. （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. Intelligence Science Program （Knowledge and Understanding）・D1. A deep systematic understanding of the advanced intelligence of human beings and its realization by computers. （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
Class Objectives /Class Outline	- To understand how to retrieve necessary information from stored databases efficiently and effectively - To understand what is valuable information and how to extract valuable information - To understand important issues for handling large-scale data
Class Schedule	1-2: Guidance and recent data innovations Overview of data mining technology and introduction of recent research results and case studies 3-4: Database management system Basics of database management systems (relational algebra, SQL query language, consistency constraints, transactions, exclusive control, etc.) 5-6: Multidimensional analysis Many facts can be understood by aggregating databases from various directions and analyzing trends. Here, you will learn and practice aggregation operations necessary for multidimensional analysis using familiar real data. 7-8: Exercise data and data mining tools The exercise uses data from the Regional Economic Analysis System (RESAS) provided by the Ministry of Economy, Trade and Industry and the Secretariat of the Council for the Realization of the Digital Garden City National Vision, Cabinet Secretariat, and analyzes various knowledge for regional revitalization through advanced data analysis. We will excavate it through. WEKA, known as a data mining tool, is used as an analysis tool. 9-10: Association rules Definition of association rules and association rule discovery algorithms (A priori algorithm, FP-Tree, FP-Growth, etc.) 11-12: Prediction model Definition of discriminant rules, their optimization methods, and decision trees/regression trees 13-14: Clustering Define data similarity and group data based on similarity 15: Data mining application Related topics such as familiar application examples such as CRM, recommendation systems, and search engines, NoSQL databases such as key-value stores, and map reduce calculation platforms will be introduced in the 15th lecture and during gaps in the lectures.
Text/Reference Books,etc.	Reference Book: Jiawei Han, Micheline Kamber共著「Data Mininig: Concepts and Technologies」(Morgan Kaufmann)
PC or AV used in Class,etc.
(More Details)	PowerPoint and Handout
Learning techniques to be incorporated
Suggestions on Preparation and Review	Compute the results of sample databases by yourself and confirm the results and how they work.
Requirements
Grading Method	We will ask you to submit a practice report.
Practical Experience	Experienced
Summary of Practical Experience and Class Contents based on it	The lecturer has been in charge of data mining research and development and product development at a corporate research institute for many years, and has a track record of introducing this technology to securities companies, hospitals, etc.
Message
Other	Since this lecture is aimed at information science students who must bring Windows PC, the exercise materials were created for Windows users. If your PC is a Mac, we recommend that you enable Windows to run on your Mac in advance. (The exercise content also works on Mac. However, advanced PC knowledge is required in order to adjust Mac-specific operations. Because there are few TAs, those who are not confident that they can adjust windows exercises for Mac on their own should use Windows on Mac.)
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

Back to syllabus main page