Introduction to Clustering

preview_player
Показать описание
"(1) Possible partitions (clutsters) of data points
(2) Goodness of partitions (clusters)"
  1. IIT Madras - B.S. Degree Programme
  2. partitions
  3. clustering
Рекомендации по теме
Introduction to Clustering 0:05:13

Introduction to Clustering

Introduction to Clustering 0:06:22

Introduction to Clustering 🔥

StatQuest: K-means clustering 0:08:31

StatQuest: K-means clustering

Introduction To Clustering 0:05:39

Introduction To Clustering | ML Course 1.4

Clustering in Machine 0:06:53

Clustering in Machine Learning

6.2.1 An Introduction 0:04:29

6.2.1 An Introduction to Clustering - Video 1: Introduction to Netflix

12. Clustering 0:50:40

12. Clustering

Introduction to Clustering 0:19:53

Introduction to Clustering & K-Means

Introduction to clustering 0:26:52

Introduction to clustering

6.2.15 An Introduction 0:03:23

6.2.15 An Introduction to Clustering - Video 8: The Analytics Edge of Recommendation Systems

6.2.5 An Introduction 0:03:26

6.2.5 An Introduction to Clustering - Video 3: Movie Data and Clustering

BigDataX: Introduction to 0:05:08

BigDataX: Introduction to clustering

Introduction to Clustering 0:17:00

Introduction to Clustering

Introduction to Clustering 0:45:34

Introduction to Clustering Algorithms

Introduction to clustering 0:07:30

Introduction to clustering

Intro to Clustering 0:04:36

Intro to Clustering

Raspberry Pi Cluster 0:08:09

Raspberry Pi Cluster Ep 1 - Introduction to Clustering

6.2.9 An Introduction 0:04:13

6.2.9 An Introduction to Clustering - Video 5: Hierarchical Clustering

Learn Cluster Analysis 0:05:31

Learn Cluster Analysis | Cluster Analysis Tutorial | Introduction to Cluster Analysis

4 Basic Types 0:08:53

4 Basic Types of Cluster Analysis used in Data Analytics

6.1.1 Welcome to 0:00:39

6.1.1 Welcome to Unit 6 - An Introduction to Clustering

Understand the Basic 0:19:05

Understand the Basic Cluster Concepts | Cluster Tutorials for Beginners

6.2.7 An Introduction 0:06:19

6.2.7 An Introduction to Clustering - Video 4: Computing Distances

6.2.11 An Introduction 0:06:47

6.2.11 An Introduction to Clustering - Video 6: Getting the Data

Комментарии
Автор

🎯 Key Takeaways for quick navigation:

03:29 📊 *Introduction to Clustering*
- Clustering is a paradigm in unsupervised learning where the goal is to partition data points into K clusters/groups, aiming to uncover underlying structures.
06:23 🎯 *Defining Performance Measure for Partitions*
- The performance measure involves computing the squared distance of each data point to the mean of its assigned cluster, emphasizing homogeneity within clusters.
09:59 🧮 *Objective: Minimizing Performance Measure*
- The objective is to minimize the sum of squared distances across all data points, achieving the best partition that optimizes the defined performance measure.
15:15 🚫 *Challenge: Exponential Possibilities*
- The naive algorithm to explore all possible partitions is impractical due to the exponential growth in the number of possibilities (K^n), making it an NP HARD problem.
16:43 ⚙️ *Heuristic Approach*
- Due to the impracticality of exact solutions, a heuristic algorithm will be introduced to address the clustering problem efficiently, considering the computational complexity.

Dextero