Introduction to Clustering using R

Course Curriculum

Chapter 1: Course Preview, Understanding the Basics of Clustering

Lecture 1: Preview, Machine Learning, clustering/classification, supervised/unsupervised

Lecture 2: clustering benefits, meaning of unlabeled data

Lecture 3: understand clustering using some examples

Lecture 4: going through the course content, target audience

Lecture 5: Different types of data – continuous/interval & binary

Lecture 6: Different types of data – ordinal & nominal data, Scaling the data

Lecture 7: create random dataset in R, some datasets used in the course

Chapter 2: Popular distance Measure – Euclidean & Hamming/ Kmeans Clustering in R

Lecture 1: Using small subset to understand euclidean distance measure

Lecture 2: Manually calculating euclidean distance

Lecture 3: Randomness in the clustering process

Lecture 4: Make two clusters using randomly chosen cluster centroids

Lecture 5: make a scatterplot in R using the identified clusters

Lecture 6: Introduction to K means clustering, k means function in R, Understanding the out

Lecture 7: imdb dataset, Output of k means function, Understanding R codes

Lecture 8: recap k means clustering process, Understand R codes

Lecture 9: Making clusters using automobile data, Understand R codes, make scatterplot in R

Lecture 10: Introduction to manhattan distance, use automobile dataset to calculate manhatta

Lecture 11: Formula manhattan distance, visualize the clusters in R

Chapter 3: Understand Partitioning Around Medoids, Hierarchical form of Clustering

Lecture 1: Understand Partitioning Around Medoids Clustering

Lecture 2: Introduction to Hierarchical Clustering

Lecture 3: Introduction to Single Linkage form of Hierarchical clustering

Lecture 4: Complete linkage form of hierarchical clustering

Lecture 5: Average linkage form of hierarchical clustering (first method)

Lecture 6: Average linkage form of hierarchical clustering (second method)

Lecture 7: hclust function in R, Represent clusters using ggplot function

Lecture 8: Ward method of Hierarchical clustering, Difference between ward.D and ward.D2

Chapter 4: Understand clustering process of binary data, Kmodes clustering

Lecture 1: Cluster Binary data, Simple Matching, Jaccard & Dice coefficient

Lecture 2: Convert single Nominal column to multiple Binary column

Lecture 3: Convert single Nominal column to multiple Binary column (part 2)

Lecture 4: Clustering process of Mixed data

Lecture 5: Introduction to Kmodes clustering

Lecture 6: Kmodes Clustering, Simple matching dissimilarity

Lecture 7: Kmodes clustering- Understanding the process

Chapter 5: Density-based clustering, cluster ordinal data, find replacement for empty cell

Lecture 1: Introduction to Density based clustering

Lecture 2: cluster ordinal data

Lecture 3: Replace Missing data to improve clustering outcome

Chapter 6: Determine ideal number of clusters, daisy function to cluster mixed data

Lecture 1: What would be the right number of clusters, Elbow method

Lecture 2: example elbow method, nbclust function in R

Lecture 3: Silhouette method, Using silhouette method in R, visualize identified clusters

Lecture 4: Example to understand Silhouette method

Lecture 5: Daisy function to cluster mixed data, Gower coefficient, Some Examples

Chapter 7: Goodbye

Lecture 1: Goodbye