Data Mining - Clustering/Segmentation Using R, Tableau

Introduction of the trainer & the agenda of the various concepts that you will learn as part of Data Science Learn  Data Mining-Clustering using R and Tableau also Tableau will be discussed.

Why do we need to do clustering and how does it help in making a business decision.What is the primary objective of Clustering; learn about it with an example.

Data Mining in a Nutshell!What are the two main branches of Data mining and their distributaries? The Two approaches to clustering, and introduction to principle of Hierarchical clustering.

Visualisation of Hierarchical clustering, Grouping of records & division of cluster based on distance measure. Rules for the measure of distance

Hierarchical clustering using a case study, Computation of distance amongst two records having multiple inputs using the eucleadian method. Understanding the need for standardization of data; Z-score, Other types of distance measures

Learn How to measure of distance between categorical data. Various measurements using binary matrix for twin category data. Distance measurement rule for for more than 2 categories.

Learn about Various distance measure criteria for between clusters. “Hands On” exercise on hierarchical clustering and summarizing using Dendrogram

Learn about Measurement of distance between records having both numerical and categorical variables. Method of creation of Dummy variable data from categorical data. The need and the method of standardization the numerical data to the same scale as categorical dummy variable data.

Learn about Measure of distance using Gower’s General Similarity coefficient for mixed data using weighted means.

Learn How Clustering helps - the consumer perspective and the supplier perspective; Other insights from clusters and their labeling

Learn about Reading an XLSX file into R; Understanding the Database (MBA); Scaling the data to make it unitless using Z Scores

Learn about The ‘hclust’ function with complete linkage; Visualization of the clustering as as dendrogram using the ‘plot’ function; splitting the dendrogram into ‘k’ clusters using the ‘cuttree’ function; assigning the records to the respective clusters

Learn about The main difference between Hierarchical and non-hierarchical clustering; similarity within, dissimilarity amongst clusters; Algorithm (or iterative steps) for K means clustering

Learn about The main difference between Hierarchical and non-hierarchical clustering; similarity within, dissimilarity amongst clusters; Algorithm (or iterative steps) for K means clustering

Learn R code for K means clustering using only two inputs; for ease in visualization; computing the ideal number of clusters; viewing the iterative process of clustering (giving and receiving clusters) as an animation

Learn about random generation of centroid at the first iteration; The explanation of ‘receiving and giving clusters’ using the euclidean distance; understanding the math of centroid and the clustering based on distance; understanding the various attributes of the k-means output.

Learn The best value for ‘k’ using scree plot; determining the value of ‘k’ based on information gain; the point of the elbow; the ‘aggregate’ function for viewing each cluster as one record and analysis of the same; Labeling of clusters

Learn about The Pros and Cons of K means clustering and Hierarchical Clustering

Learn about Selection of ‘k’ based on the simplicity or adequacy; risks with the ‘k’ value - Local Minima problem; Cross checking of clusters for consistency;

Learn about accelerometer data set for analysing the steps taken by different user as tracked by their smart phones; Importance of Domain knowledge

Learn about Data Mining unsupervised; Hierarchical Clustering; Non Hierarchical Clustering; Distance measure for continuous and discrete; Types of Linkage; Dendrogram;  Sum of Sqares distances between & within Clusters