Data Mining (DBM302m)
Vietnamese title: Khai phá dữ liệu
Credits: 3

Description:
- This course introduces the key steps involved in the data mining pipeline, including data understanding, data preprocessing, data warehousing, data modeling, interpretation and evaluation, and real-world applications.
- Students will learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining. This courser will introduce methods for pattern-based classification and some interesting applications of pattern discovery. It also provides you the opportunity to learn skills and content to practice and engage in scalable pattern discovery methods on massive transactional data, discuss pattern evaluation measures, and study methods for mining diverse kinds of patterns, sequential patterns, and sub-graph patterns.

Learning outcomes:
Understand what is data mining and what are key components of the data mining pipeline
Learn and apply one of the basic core of data mining: data visualization
Understand and apply one subtield in data mining: patern discovery, practice with the data visualization of data mining
Apply another subtield in data mining: cluster analysis

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 FU FLM at https://flm.fpt.edu.vn/ for up-to-date course information

Tools:
Jupyter Notebooks, RStudio IDE

Materials:
description: Data Mining Pipeline
publisher: Coursera
note: https://www.coursera.org/learn/data-mining-pipeline?specialization=data-mining-foundations-practice
description: Data Visualization
publisher: Coursera
note: https://www.coursera.org/learn/datavisualization?specialization=data-mining
description: Pattern Discovery in Data Mining
publisher: Coursera
note: https://www.coursera.org/learn/data-patterns?specialization=data-mining
description: Cluster Analysis in Data Mining
publisher: Coursera
note: https://www.coursera.org/learn/cluster-analysis?specialization=data-mining

Assessments:
category: Lab 2
type: on-going
weight: 15.0
duration: 45'
question_type: CLO2
no_question: Home work, class
knowledge_and_skill: 1
grading_guide: - cover chapter 2
note: in class, by instructor
category: Lab 3
type: on-going
weight: 20.0
duration: 45'
question_type: CLO3
no_question: Home work, class
knowledge_and_skill: 1
grading_guide: - cover chapter 3
note: in class, by instructor
category: Lab 4
type: on-going
weight: 20.0
duration: 45'
question_type: CLO4
no_question: Home work, class
knowledge_and_skill: 1
grading_guide: - cover chapter 4
note: in class, by instructor
category: Progress test
type: on-going
weight: 15.0
duration: 45'
question_type: CLO1,CLO2, CLO3
no_question: Multiple choices
Marked by Computer or a suitable format
knowledge_and_skill: 40
grading_guide: - cover chapter 1, 2, 3
note: in class, by instructor
category: Final exam
type: Final exam
weight: 30.0
duration: 60'
question_type: CLO1, CLO2, CLO3, CLO4
no_question: Multiple choices
Marked by Computer
knowledge_and_skill: 50
grading_guide: - cover chapter 1, 2, 3, 4
note: by exam board,
using computer

Schedule:
session: - Course Introduction
- Chapter 1:
1.1.Introduction to Data mining
1.2.Introduction to Data mining Pipeline
learning_outcomes: IT
materials: mat1
session: 1.3 Data understanding - Objects & Attributes, Statistics, Visual
1.4 Data understadining - Data similarity
learning_outcomes: T
materials: mat2
session: 1.5 Data Preprocessing - Cleaning, Integration, Transformation, Reduction
learning_outcomes: T
session: 1.6 Data warehousing - Warehousem Cube and OLAP
1.7 Data warehousing - Data Warehouse Architecture
learning_outcomes: T
session: - Chapter 2: Data visualization
2.1 Introduction of data visualization
learning_outcomes: T
session: 2.2 Visualization of Numerical Data
learning_outcomes: T
session: 2.3 Visualization of Non-Numerical Data
learning_outcomes: T
session: 2.4 The Visualization of Dashboard
learning_outcomes: T
session: Lab 2.1
learning_outcomes: T U
session: Lab 2.2
learning_outcomes: T U
session: Review Lab 2.1, 2.2
Chapter 3: Pattern Discovery in Data mining
3.1 Introduction to Pattern Discovery 1
3.1.1 What Is Pattern Discovery? Why Is It Important?
3.1.2 Frequent Patterns and Association Rules
learning_outcomes: T
session: Introduction to Pattern Discovery 2 3.1.3 Compressed Representation: Closed Patterns and Max-Patterns
3.1.4 The Downward Closure Property of Frequent Patterns
learning_outcomes: T
session: 3.2 The Apriori Algorithm
learning_outcomes: T
session: 3.3 Extensions or Improvements of Apriori
learning_outcomes: T
session: 3.4 Mining Frequent Patterns by Exploring Vertical Data Format
learning_outcomes: T
session: 3.5 FPGrowth: A Pattern Growth Approach
learning_outcomes: T
session: 3.6 Mining Closed Patterns
learning_outcomes: T
session: Lab 3.1
learning_outcomes: TU
session: 3.7 Pattern Evaluation 1 3.7.1. Limitation of the Support-Confidence Framework 3.7.2. Interestingness Measures: Lift and χ
learning_outcomes: T
session: 3.7 Pattern Evaluation 2 3.7.3. Null Invariance Measures 3.7.4. Comparison of Null-Invariant Measures
learning_outcomes: T
session: 3.8 Mining methods 1 3.8.1. Mining Multi-Level Associations
3.8.2. Mining Multi-Dimensional Associations
3.8.3. Mining Quantitative Associations
learning_outcomes: T
session: 3.8 Mining methods 2 :
3.8.4. Mining Negative Correlations
3.8.5. Mining Compressed Patterns
learning_outcomes: T
session: Lab 3.2
learning_outcomes: TU
session: 3.9 Mining sequential patterns 3.9.1. Sequential Pattern and Sequential Pattern Mining
3.9.2. GSP: Apriori-Based Sequential Pattern Mining
learning_outcomes: T
session: 3.9 Mining sequential patterns 2 3.9.3. SPADE—Sequential Pattern Mining in Vertical Data Format
3.9.4. PrefixSpan—Sequential Pattern Mining by Pattern-Growth
3.9.5. CloSpan—Mining Closed Sequential Patterns
learning_outcomes: T
session: 3.10 Mining spatiotemporal and trajectory patterns 1 3.10.1. Mining Spatial Associations 3.10.2. Mining Spatial Colocation Patterns 3.10.3. Mining and Aggregating Patterns over Multiple Trajectories
learning_outcomes: T
materials: flm.fpt.edu.vn
session: 3.10 Mining spatiotemporal and trajectory patterns 2 3.10.4. Mining Semantics-Rich Movement Patterns 3.10.5. Mining Periodic Movement Patterns
learning_outcomes: T
session: Lab 3.3
learning_outcomes: TU
session: 3.11 Mining quality phrases 1 3.11.1. From Frequent Pattern Mining to Phrase Mining 3.11.2. Previous Phrase Mining Methods
learning_outcomes: T
session: 3.11 Mining quality phrases 2 3.11.3. ToPMine: Phrase Mining without Training Data 3.11.4. SegPhrase: Phrase Mining with Tiny Training Sets
learning_outcomes: T
session: 3.12 Advanced topics on pattern discovery 1 3.12.1. Frequent Pattern Mining in Data Streams 3.12.2. Pattern Discovery for Software Bug Mining 3.12.3. Pattern Discovery for Image Analysis
learning_outcomes: T
session: 3.12 Advanced Topics on Pattern Discovery 2 3.12.4 Pattern Mining and Society—Privacy Issue 3.12.5. Looking Forward
learning_outcomes: T
session: Lab 3.4
learning_outcomes: TU
session: Review Lab 3.1, 3.2, 3.3, 3.4
session: Review Chapter 1-2-3
learning_outcomes: I
materials: flm.fpt.edu.vn
session: Progress Test 1
learning_outcomes: U
session: Chapter 4: Cluster Analysis in Data mining
4.1 Introduction to Cluster Analysis 1 4.1.1. What is Cluster Analysis 4.1.2. Applications of Cluster Analysis 4.1.3 Requirements and Challenges
learning_outcomes: T
session: 4.1 Introduction to Cluster Analysis 2 4.1.4 A Multi-Dimensional Categorization 4.1.5 An Overview of Typical Clustering Methodologies 4.1.6 An Overview of Clustering Different Types of Data 4.1.7 An Overview of User Insights and Clustering
learning_outcomes: T
session: 4.2 Similarity Measures for Cluster Analysis 1 4.2.1 Basic Concepts: Measuring Similarity between Objects
4.2.2 Distance on Numeric Data Minkowski Distance
4.2.3 Proximity Measure for Symetric vs Asymmetric Binary Variables
learning_outcomes: T
session: 4.2 Similarity Measures for Cluster Analysis 2
4.2.4 Distance between Categorical Attributes Ordinal Attributes and Mixed Types
4.2.5 Proximity Measure between Two Vectors Cosine Similarity
4.2.6 Correlation Measures between Two variables Covariance and Correlation Coefficient
learning_outcomes: T
session: Lab 4.1
learning_outcomes: TU
session: 4.3 Partitioning-Based Clustering Methods 1
4.3.1 Partitioning-Based Clustering Methods
4.3.2 K-Means Clustering Method
learning_outcomes: T
session: 4.3 Partitioning-Based Clustering Methods 2
4.3.3 Initialization of K-Means Clustering
4.3.4 The K-Medoids Clustering Method
learning_outcomes: T
session: 4.3 Partitioning-Based Clustering Methods 3
4.3.5 The K-Medians and K-Modes Clustering Methods
4.3.6 Kernel K-Means Clustering
learning_outcomes: T
session: 4.4 Hierarchical Clustering Methods 1
4.4.1 Hierarchical Clustering Methods
4.4.2 Agglomerative Clustering Algorithms
learning_outcomes: T
session: 4.4 Hierarchical Clustering Methods 2
4.4.3 Divisive Clustering Algorithms
4.4.4 Extensions to Hierarchical Clustering
4.4.5 BIRCH: A Micro-Clustering-Based Approach
learning_outcomes: T
session: Lab 4.2
learning_outcomes: TU
session: 4.4 Hierarchical Clustering Methods 3
* Overview
4.4.6 CURE: Clustering Using Well-Scattered Representatives
4.4.7 CHAMELEON: Graph Partitioning on the KNN Graph of the Data
learning_outcomes: T
materials: flm.fpt.edu.vn
session: 4.4 Hierarchical Clustering Methods 3

4.4.8 Probabilistic Hierarchical Clustering
learning_outcomes: T
materials: flm.fpt.edu.vn
session: 4.5 Density-Based and Grid-Based Clustering Methods
Overview
4.5.1 Density-Based and Grid-Based Clustering Methods
4.5.2 DBSCAN: A Density-Based Clustering Algorithm
learning_outcomes: T
session: 4.5 Density-Based and Grid-Based Clustering Methods 2
4.5.3 OPTICS: Ordering Points To Identify Clustering
Structure
4.5.4 Grid-Based Clustering Methods
learning_outcomes: T
session: 4.5 Density-Based and Grid-Based Clustering Methods 3
4.5.5 STING: A Statistical Information Grid Approach
4.5.6 CLIQUE: Grid-Based Subspace Clustering
learning_outcomes: T
session: Lab 4.3
learning_outcomes: TU
session: 4.6 Methods for Clustering Validation 1
Overview
4.6.1 Methods for Clustering Validation
4.6.2 Clustering Evaluation Measuring Clustering Quality
4.6.3 Constraint-Based Clustering
learning_outcomes: T
session: 4.6 Methods for Clustering Validation 2
4.6.4 External Measures 1: Matching-Based Measures
4.6.5 External Measure 2: Entropy-Based Measures
learning_outcomes: T
session: 4.6 Methods for Clustering Validation 3
4.6.6 External Measure 3: Pairwise Measures
4.6.7 Internal Measures for Clustering Validation
learning_outcomes: T
session: 4.6 Methods for Clustering Validation 4
4.6.8 Relative Measures
4.6.9 Cluster Stability
4.6.10 Clustering Tendency
learning_outcomes: T
session: Lab 4.4
learning_outcomes: TU
session: Review lab 4.1, 4.2, 4.3, 4.4
learning_outcomes: U
session: Review
learning_outcomes: I