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Welcome to Clustering (Theory & Code)

01 Unsupervised Learning (Theory)

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

02 Clustering (Theory)

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

03 Euclidean & Manhattan Distance (Theory)

  • 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

04 K-Means Clustering (Theory)

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

05 Elbow Method (Theory)

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

06 K Means Clustering (Python Code)

  • 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

07 Hierarchical Clustering (Theory)

  • 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

08 Dendogram (Theory)

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

09 Hierarchical Clustering (Python Code)

  • 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

10 DBScan Clustering (Theory)

  • 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

11 DBScan Clustering (Python Code)

  • 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

12 GMM Clustering (Theory)

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

13 Gausian Mixture Model Clustering (Python Code)

  • 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

14 Cluster Adjustment (Theory)

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

15 Silhouette Coefficient - Cluster Validation (Theory)

  • 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

16 Disadvantage & Choosing Right Clustering Method (Theory)

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

17 Clustering Revision (Theory)

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

18 Interview Questions on Clustering (Theory)

  • Commonly asked question on Clustering

19 K Modes (Theory)

  • 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

  • End.

  • It uses Mode for most value in the column

20 K Modes (Python Code)

  • K Mode code in Python