Learn · Data Mining for Decision Making
Lecture 24 of 33Week 4
Market Basket Analysis Explained: Discovering Hidden Patterns
Prof. Varun Dutt · IIT Mandi
Credits and licence
- From
- Data Mining for Decision Making, IIT Mandi (at NPTEL)
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge. Coordinated by IIT Madras.
- Who can watch
- Tweens profiles and older. How courses are placed
All the lectures
- Data Mining Explained: Decision Lists & Association Rules Made Simple
- How to Install WEKA: Step-by-Step Guide for Beginners
- Getting Started with WEKA: A Complete Beginner’s Overview
- Basics of Bayesian Classification: Understanding Probabilistic Learning
- A Deep Dive into Bayesian Classification: Concepts & Intuition building
- Implementing Bayesian Classification Using WEKA
- Bayesian Classification with Numeric Data: Theory & Examples
- Decision Trees Explained: From Roots to Leaves
- Building Decision Trees in WEKA: Hands-On Tutorial
- Neural Networks Fundamentals: How Artificial Brains Learn
- Neural Networks Explained: Architecture & Learning
- Implementing Neural Networks in WEKA
- Clustering Basics: Grouping Data Without Labels
- Clustering Algorithms in WEKA: Practical Demonstration
- Clustering Techniques Explained: Beyond the Basics
- Principal Component Analysis (PCA): Dimensionality Reduction Explained
- Applying PCA in WEKA: Step-by-Step Walkthrough
- Linear Regression Explained: Modeling Relationships in Data
- Linear Regression Using WEKA: Practical Implementation
- Logistic Regression Basics: Classification Made Easy
- Logistic Regression in WEKA: Hands-On Classification
- Instance-Based Learning Explained: Learning from Examples
- Instance-Based Learning in WEKA: KNN and Beyond
- Market Basket Analysis Explained: Discovering Hidden Patterns
- Market Basket Analysis Using WEKA
- Linear Discriminant Analysis (LDA): Classification Intuition
- Linear Discriminant Analysis Explained in Depth
- Implementing LDA in WEKA
- Model Evaluation Metrics Explained: Accuracy, Precision & More
- Support Vector Machines Explained: Maximum Margin Learning
- Support Vector Machines Deep Dive: Kernels & Optimization
- Advanced SVM Concepts: Kernels, Margins & Hyperparameters
- Support Vector Machines in WEKA: Practical Tutorial