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• What is Unsupervised Learning & Goals of Unsupervised Learning
• Type of Unsupervised Learning: 1.Clustering, 2.Association Rule & 3.Dimensionality Reduction

• Definition and Application of Clustering
• 4 methods: 1.K Means 2.Hierarchical 3.DBScan & 4.Gaussian Mixture

• Two points are near to each other, chances they are similar
• Distance Measure between two points
  1. Euclidean Distance: Under-root of Square distance between two points
  2. Manhattan Distance: Absolute Distance between points

• How Algorithim works (Step Wise Calculation)
• Pre-processing required for K Means
• Determining optimal number of K: 1.Profiling Approach & 2.Elbow Method

• Working of Elbow Method with Example
• 3 concepts: 1.Total Error, 2.Variance/Total Squared Error & 3.Within Cluster Sum of Square (WCSS)

• Define number of clusters, take centroids and measure distance
• Euclidean Distance : Measure distance between points
• Number of Clusters defined by Elbow Method
• Elbow Method : WCSS vs Number of Cluster
• Silhouette Score : Goodness of Clustering

• Two Approaches: 1.Agglomerative(Botton-Up) & 2.Divisive(Top-Down)
• Types of Linkages:
  1. Single Linkage - Nearest Neighbour (Minimal intercluster dissimilarity)
  2. Complete Linkage - Farthest Neighbour (Maximal intercluster dissimilarity)
  3. Average Linkage - Average Distance (Mean intercluster dissimilarity)
• Steps in Agglomerative Hierarchical Clustering with Single Linkage
• Determining optimal number of Cluster: Dendogram

• Hierarchical relationship between objects
• Optimal number of Clusters for Hierarchical Clustering

• Type of HC
  1. Agglomerative : Bottom Up approach
  2. Divisive : Top Down approach
• Number of Clusters defined by Dendogram
• Dendogram : Joining datapoints based on distance & creating clusters
• Linkage : To calculate distance between two points of two clusters
  1. Single linkage : Minimum Distance between two clusters
  2. Complete linkage : Maximum Distance between two clusters
  3. Average linkage : Average Distance between two clusters

• Density Based Clustering
• Kmeans & Hierarchical good for compact & well seperated Data
• Both are sensitive to Outliers & Noise
• DBScan overcome all the issue & works well with Outliers
• 2 important parameters -
  1. eps: Distance between 2 points is lower/equal to eps they are neighbours
  2. MinPts: Minimum number of neighbours/data points with eps radius

• No need to give pre-define clusters
• Distance metric is Euclidean Distance
• Need to give 2 parameters
  1. eps : Radius of the circle
  2. min_samples : minimum data points to consider it as clusters

• Weakness of K Means
• Expectation Maximization(EM) method

• Probablistic Model
• Uses Expectation-Minimization (EM) steps:
  1. E Step : Probability of datapoint of each cluster
  2. M Step : For each cluster,revise parameter based on proabability

• 2 Steps we normally do for Cluster Adjustement
  1. Quality of Clustering (Cardinality & Magnitude)
  2. Performance of Similiarity Measure (Euclidean Distance)

• Clusters are well apart from each other as the silhouette score is closer to 1
• It is a metric used to calculate the goodness of a clustering technique
• Its value ranges from -1 to 1.
  1. 1: Means clusters are well apart from each other and clearly distinguished
  2. 0: Means clusters are indifferent, or distance between clusters is not significant
  3. -1: Means clusters are assigned in the wrong way

• Disadvantage of each clustering techniques respectively
• Based on the data, which is the right clustering method

• Short Description of Each Clustering Alogrithim
• Advantage, Disadvantage
• When to use what

• Commonly asked question on Clustering

• For Categorical variable clustering, use K Modes
• It uses the dissimilarities(total mismatch) between data points
• Lesser the dissimilarities, the more our data points are closer
• It uses Mode for most value in the column

• K Mode code in Python