Data Mining (DBM301)
Vietnamese title: Khai phá dữ liệu
Credits: 3

Description:
Data mining is a multidisciplinary field, drawing work from areas including database technology, machine learning, statistics, pattern recognition, information retrieval, neural networks, knowledge-based systems, artificial intelligence, high-performance computing, and data visualization. This course presents techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. As a result, the course is a comprehensive introduction to data mining, presented with effectiveness and scalability issues in focus. It should be useful for computing science students, application developers, and business professionals at master degree, as well as researchers involved in any of the disciplines listed above.

Learning outcomes:
Describe main concepts of Data mining
Describe the Outlier Detection
Understand Data Mining Trends and Research Frontiers
Describe the Data
Describe the Data Preprocessing
Understand the Weka tool
Describe the Data Warehouse & OLAP Technology
Describe the Data Cube Technology
Describe the Mining Frequent Patterns, Association Rule Mining and Correlation
Apply the Classification
Apply the Clustering

Student tasks:
- Students must attend at least 80% of contact slots in order to be accepted to the final examination.
- Student is responsible to do all exercises, assignments and labs given by instructor in class or at home and submit on time
- Use laptop in class only for learning purpose
- Promptly access to the https://flm.fpt.edu.vn/ for up-to-date course information

Tools:
- Internet
- Matlab trial version, Python

Materials:
description: Data Mining: Concepts and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems)
http://library.books24x7.com
author: Jiawei Han, Micheline Kamber and Jian Pei
publisher: Morgan Kaufmann Publishers
published_date: 2012
edition: 3rd
isbn: 978-0-12-381479-1

Assessments:
category: Assignment
type: on-going
weight: 30.0
duration: 90' in class
no_question: Theory present or project
grading_guide: technical aspect of studied topics
note: - Guided by instructor
- Application or Theory
- Work in group
category: Exercise
type: on-going
weight: 40.0
duration: 10'-30'
no_question: Any appropriate type
knowledge_and_skill: 10
note: in class, by instructor
category: Final exam
type: Final exam
weight: 30.0
duration: 30'
no_question: Multiple choicesMarked by Computer
knowledge_and_skill: 30
grading_guide: concepts, algorithms;
all studied chapters
note: by exam board,
using computer

Schedule:
session: Course Introduction
Introduction to Data mining
materials: DBM301
session: Course Introduction
Introduction to Data mining
materials: DBM301
session: Getting to Know Your Data
session: Getting to Know Your Data
session: Data Preprocessing
session: Data Preprocessing
session: Data Preprocessing
session: Data Preprocessing
session: Introduction to Weka & Data Prepocessing
session: Introduction to Weka & Data Prepocessing
session: Introduction to Weka & Data Prepocessing
session: Introduction to Weka & Data Prepocessing
session: Data Warehouse & OLAP Technology
session: Data Warehouse & OLAP Technology
session: Data Warehouse & OLAP Technology
session: Data Warehouse & OLAP Technology
session: Check Assignment 1
session: Check Assignment 1
session: Check Assignment 1
session: Check Assignment 1
session: Data Cube Technology
session: Data Cube Technology
session: Data Cube Technology
session: Data Cube Technology
session: Mining Frequent Patterns, Association Rule Mining and Correlation
session: Mining Frequent Patterns, Association Rule Mining and Correlation
session: Mining Frequent Patterns, Association Rule Mining and Correlation
session: Mining Frequent Patterns, Association Rule Mining and Correlation
session: Exercise 1
session: Exercise 1
session: Exercise 1
session: Exercise 1
session: Classification
session: Classification
session: Classification
session: Classification
session: Exercise 2
session: Exercise 2
session: Exercise 2
session: Exercise 2
session: Cluster
session: Cluster
session: Cluster
session: Cluster
session: Exercise 3
session: Exercise 3
session: Exercise 3
session: Exercise 3
session: Outlier Detection
session: Outlier Detection
session: Data Mining Trends
and Research Frontiers
session: Data Mining Trends
and Research Frontiers
session: Exercise 4
session: Exercise 4
session: Exercise 4
session: Exercise 4
session: Check Assignment 2
session: Check Assignment 2
session: Check Assignment 2
session: Check Assignment 2