Semester: 2, 4

Credit:3

Program:Computer Science

Course Module:Specialized Subjects

Responsible: Yu Mei

E-mail: yumei@tju.edu.cn

Department:College of Intelligence and Computing, Tianjin University

Time Layout(1 credit hour = 45 minutes)

8

16

24

0

0

8

Pre-requirements

Ÿ Mathematical statistics and analysis: concepts and methods

Ÿ Data structure: data storage and query method

Course Objectives

This coursediscusses basic concepts ofdata mining to help studentsfind potential knowledge. After this course, students should be able to:

Ÿ Be familiar with the overall process of data analysis and data mining, master the methods of data preprocessing, regression, classification, clustering, frequent pattern mining and outlier detection, and master the application of data warehouse and related software.

Ÿ Be familiar with the application scenarios of various tasks of data mining, and be able to choose appropriate methods to achieve basic data analysis according to actual data and needs.

Ÿ Improve presentation skills and document writing.

CourseSyllabus

Ÿ Data mining overview: definition, task, mining object

Ÿ Data: attributes, basic statistical description, similarity and dissimilarity

Ÿ Data preprocessing: data, data quality issues, data preprocessing

Ÿ Data warehousing and OLAP: design, implement, OLPA, metadata model

Ÿ Regression analysis: basic concepts, univariate linear regression, multiple linear regression, Polynomial regression

Ÿ Association analysis: definition, task, Apriori algorithm, FP- tree algorithm

Ÿ Clustering: definition, main method

Ÿ Classify: definition, decision tree, Naive Bayesian

Ÿ Classification methods, such as decision tree, naive bayesian, and neural network

Ÿ Exception Mining: definition, application, exception data generation causes, solutions

Text Book & References

Ÿ Yu Mei, Yu Jian. Data Analysis and Data Mining (2nd Edition). Tsinghua University Press, 2020.

Ÿ Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, China Machine Press,2006.

Ÿ D. Hand, H.Mannila and P. Smith, Principle of Data Mining, Springer, 2004.

ŸPang-Ning Tan,Michael SteinbachandVipin Kumar, Introduction to Data Mining, Addison Wesley, 2005.

Ÿ Te-Ming Huang, Vojislav Kecman and Ivica Kopriva, Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised and Unsupervised Learning, Springer, 2006.

Capability Tasks

CT2：Understand the basic concepts and steps of data mining.

CT3：Master related algorithms, such as OLAP, classification, clustering, and prediction.

CT4：Implement data mining’s algorithms under the particular environment.

CS1：Master the basic theories ofdata mining, and understand the development status and trends ofdata mining.

CS2：Grasp the top-ten processing algorithms of data mining to develop a system.

Achievements

Ÿ Understand what problems that data mining technology could handle with.- Level: M

Ÿ Grasp OLAP algorithm, classification, clustering, prediction algorithms. - Level: M

Ÿ Apply data mining technology to solve practical problems. - Level: M

Ÿ Use programming language to implement algorithms in data mining. - Level: M

Students:Computer Science, year 1, 2

Assessment：

Exam

Assignment

Report

TermPaper

Presentation

Others

√

√

√

Languageofassessment：Chinese

Attendance：5 % Homework: 35 %

Mid-termreport/test：30% Finalreport/test：30 %