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Cluster Analysis in Data Mining
  • Level Foundation
  • المدة 17 ساعات hours
  • الطبع بواسطة University of Illinois Urbana-Champaign
  • Offered by

عن

Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, and density-based methods such as DBSCAN/OPTICS. Moreover, learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.

الوحدات

About the Course

    • 1 Videos
    • 3 Readings
    • 1 Discussion
    • 1 Plugin
    • 1 Assignment
Show info about module content

1 Assignment

  • Orientation Quiz

1 Discussions

  • Getting to Know Your Classmates

1 Videos

  • Course Introduction

3 Readings

  • Syllabus
  • About the Discussion Forums
  • Social Media

Lesson 1: Cluster Analysis: An Introduction

    • 7 Videos
    • 1 Readings
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7 Videos

  • 1.1. What is Cluster Analysis
  • 1.2. Applications of Cluster Analysis
  • 1.3 Requirements and Challenges
  • 1.4 A Multi-Dimensional Categorization
  • 1.5 An Overview of Typical Clustering Methodologies
  • 1.6 An Overview of Clustering Different Types of Data
  • 1.7 An Overview of User Insights and Clustering

1 Readings

  • Lesson 1 Overview

Lesson 1 Graded Activities

    • 1 Assignment
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1 Assignment

  • Lesson 1 Quiz

Lesson 2: Similarity Measures for Cluster Analysis

    • 6 Videos
    • 1 Readings
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6 Videos

  • 2.1 Basic Concepts: Measuring Similarity between Objects
  • 2.2 Distance on Numeric Data Minkowski Distance
  • 2.3 Proximity Measure for Symetric vs Asymmetric Binary Variables
  • 2.4 Distance between Categorical Attributes Ordinal Attributes and Mixed Types
  • 2.5 Proximity Measure between Two Vectors Cosine Similarity
  • 2.6 Correlation Measures between Two variables Covariance and Correlation Coefficient

1 Readings

  • Lesson 2 Overview

Week 1 Graded Activities

    • 1 Assignment
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1 Assignment

  • Lesson 2 Quiz

Lesson 3: Partitioning-Based Clustering Methods

    • 6 Videos
    • 1 Readings
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6 Videos

  • 3.1 Partitioning-Based Clustering Methods
  • 3.2 K-Means Clustering Method
  • 3.3 Initialization of K-Means Clustering
  • 3.4 The K-Medoids Clustering Method
  • 3.5 The K-Medians and K-Modes Clustering Methods
  • 3.6 Kernel K-Means Clustering

1 Readings

  • Lesson 3 Overview

Lesson 3 Graded Activities

    • 1 Programming
    • 1 Assignment
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1 Programming

  • Implementing the K-means Clustering Algorithm

1 Assignment

  • Lesson 3 Quiz

Lesson 4: Hierarchical Clustering Methods

    • 5 Videos
    • 1 Readings
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5 Videos

  • 4.1 Hierarchical Clustering Methods
  • 4.2 Agglomerative Clustering Algorithms
  • 4.3 Divisive Clustering Algorithms
  • 4.4 Extensions to Hierarchical Clustering
  • 4.5 BIRCH: A Micro-Clustering-Based Approach

1 Readings

  • Lesson 4 Part 1 Overview

ClusterEnG

    • 4 Videos
    • 1 Readings
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4 Videos

  • ClusterEnG Overview
  • ClusterEnG: K-Means and K-Medoids
  • ClusterEnG Application: AGNES
  • ClusterEnG Application: DBSCAN

1 Readings

  • ClusterEnG Introduction

Lesson 4: Hierarchical Clustering Methods (continued)

    • 3 Videos
    • 1 Readings
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3 Videos

  • 4.6 CURE: Clustering Using Well-Scattered Representatives
  • 4.7 CHAMELEON: Graph Partitioning on the KNN Graph of the Data
  • 4.8 Probabilistic Hierarchical Clustering

1 Readings

  • Lesson 4 Part 2 Overview

Lesson 4 Graded Activities

    • 1 Assignment
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1 Assignment

  • Lesson 4 Quiz

Lesson 5: Density-Based and Grid-Based Clustering Methods

    • 6 Videos
    • 1 Readings
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6 Videos

  • 5.1 Density-Based and Grid-Based Clustering Methods
  • 5.2 DBSCAN: A Density-Based Clustering Algorithm
  • 5.3 OPTICS: Ordering Points To Identify Clustering Structure
  • 5.4 Grid-Based Clustering Methods
  • 5.5 STING: A Statistical Information Grid Approach
  • 5.6 CLIQUE: Grid-Based Subspace Clustering

1 Readings

  • Lesson 5 Overview

Lesson 5 Graded Activities

    • 1 Assignment
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1 Assignment

  • Lesson 5 Quiz

Lesson 6: Methods for Clustering Validation

    • 10 Videos
    • 1 Readings
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10 Videos

  • 6.1 Methods for Clustering Validation
  • 6.2 Clustering Evaluation Measuring Clustering Quality
  • 6.3 Constraint-Based Clustering
  • 6.4 External Measures 1: Matching-Based Measures
  • 6.5 External Measure 2: Entropy-Based Measures
  • 6.6 External Measure 3: Pairwise Measures
  • 6.7 Internal Measures for Clustering Validation
  • 6.8 Relative Measures
  • 6.9 Cluster Stability
  • 6.10 Clustering Tendency

1 Readings

  • Lesson 6 Overview

Week 4 Graded Activities

    • 1 Programming
    • 1 Assignment
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1 Programming

  • Implementing Clustering Validation Measures

1 Assignment

  • Lesson 6 Quiz

Wrapping Up the Course

    • 1 Discussion
    • 1 Plugin
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1 Discussions

  • Final Reflections

Auto Summary

Explore the fundamentals of cluster analysis in data mining through this foundation-level course offered by Coursera. Delve into various clustering methodologies, algorithms, and applications, including k-means, BIRCH, and DBSCAN/OPTICS. Learn techniques for clustering validation and quality evaluation, and see practical examples. Ideal for data science and AI enthusiasts, the course spans 1020 minutes and offers a starter subscription option.

Jiawei Han
Instructor

Jiawei Han