Machine Learning 102 Workshop at SP Jain

Unsupervised Learning – I : Cluster Analysis ​1. ​Basic Concepts ​2. ​Partitioning Methods 2.1. K-means 2.2. K-medoids 3. Hierarchical Methods 3.1. Diana 4. Density based Methods 4.1. DBSCAN 5. Evaluation of Clustering 6. Applications with real world problems Unsupervised Learning- II : Associations & Correlations 1. Basic Concepts 2. Frequent Itemset Mining Methods

2.1. Apriori

2.2. Improving the Efficiency of Apriori

2.3. FPGrowth

2.4. ECLAT: Frequent Pattern Mining with Vertical Data Format

3. Pattern Evaluation Methods

4. Applications with real world problems

Slides

• Introduction

• Cluster Analysis

• Cluster Analysis with R

• Association Rule Mining

• Association Rule Mining with R

• R Reference Card for Data Mining

Software and Datasets

Some software/tools and datasets needed for the training as below.

• R

• RStudio

  • R packages: arules, arulesViz, cluster, fpc

  • Datasets:

    • titanic.raw.rdata

  • mushroom