Page 20 - Full Stack Development

 Categorical - Binary Euclidean, Simple Matching Coefficient, Jaquard’s Coefficient

 Mixed - Gower’s General Dissimilarity Coefficient

 Types of Linkages

 Single Linkage / Nearest Neighbour

 Complete Linkage / Farthest Neighbour

 Average Linkage

 Centroid Linkage

 Hierarchical Clustering / Agglomerative Clustering
Description: In this continuation lecture learn about K means Clustering, Clustering ratio and
various clustering metrics. Get introduced to methods of making optimum clusters.

 Non-clustering

 K-Means Clustering

 Measurement metrics of clustering - Within Sum of Squares, Between Sum of Squares,
Total Sum of Squares

 Choosing the ideal K value using Scree plot / Elbow Curve

 Additional videos are provided to understand K-Medians, K-Medoids, K-Modes,
Clustering Large Applications (CLARA), Partitioning Around Medoids (PAM),
Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ordering
Points To Identify the Clustering Structure (OPTICS)
Description: Learn one of the most important topic Association rules in data mining.
Understand how the Apriori algorithm works, and the association rule mining algorithm.

 What is Market Basket / Affinity Analysis
 Measure of association

 Support

 Confidence

 Lift Ratio

 Apriori Algorithm

 Sequential Pattern Mining

 Data Mining Unsupervised - Recommender System
Classifiers

 Machine Learning Classifiers - KNN

 Classifier - Naive Bayes

 Decision Tree And Random Forest

 Survival Analysis