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Unsupervised Learning - Preliminaries

  • July 15, 2023
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Meet the Author : Mr. Bharani Kumar

Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 18+ years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.

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Distance Calculation

Distance is either calculated between: distance calculation

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Distance Properties:

  • Should be non-negative (distance > 0)
  • Distance between a record to itself is equal to 0
  • Satisfies Symmetry (Distance between records 'i' & 'j' is equal to the distance between records 'j' & 'i')

If the variables scale or have different units, standardise or normalise the variables before computing the distance.

distance properties

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Distance Calculations

Distance Metrics for Continuous Data

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distance calcutaions

Distance Metrics for Binary Categorical Data

  • Binary Euclidean Distance
  • Simple Matching Coefficient
  • Jaccard's Coefficient

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Distance Metrics for Categorical Data (> 2 categories)

  • Distance is 0, if both items have same category
  • Distance is 1 otherwise

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Distance Metrics when both Quantitative Data & Categorical Data exists in a dataset

  • Gower's General Dissimilarity Coefficient

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Linkages - Distance between a record & a cluster, or between two clusters.

  • Single Linkage - This is the closest a record may be to a cluster or to another cluster.

    • Single Linkage is also called as Nearest Neighbor
    • Emphasis is on close records or regions and not on overall structure of Data
    • Capable of clustering non-elliptical shaped regions
    • Gets influenced greatly by outliers or noisy data


  • Complete Linkage - The diameter between a record and a cluster, or between two clusters, is the greatest.

    • Complete Linkage is also called as Farthest Neighbor
    • Complete Linkage is also sensitive to outliers
    complete linkage complete linkage


  • Average Linkage - This is the mean distance between any two clusters or between any two records.

    • Average Linkage is also called Group Average
    • Very expensive because computation takes a lot of time


  • Centroid Linkage - This is the separation between two clusters' centroids, or between a cluster's record and centroid.

    • Centroid Linkage is also called Centroid Similarity


  • Ward's Criterion - By combining them into a single cluster, the SSE criteria for clustering's value increased.

    • This is also called Ward's Minimum Variance and it minimizes the total within cluster variance


  • Group Averaged Agglomerative Clustering (GAAC)

    • Two clusters are merged based on cardinality of the clusters and centroid of clusters
    • Cardinality is the number of elements in the cluster


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