Unsupervised Learning in Machine Learning > Unsupervised Learning Algorithms

K-Means Clustering - Practical

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Unsupervised Learning algorithm K-Means Clustering divides the unlabeled dataset into various clusters. K specifies the number of pre-defined clusters that must be produced during the process; for example, if K=2, two clusters will be created, and if K=3, three clusters will be created, and so on.

Pick k items at random from the dataset to serve as cluster representatives. Using a Euclidean distance derived by a similarity function, link each remaining item in the collection to the closest cluster representative. Recalculate the representatives of the new clusters.

  • Choose the number of clusters k.
  • select k random points from the data as centroids.
  • Assign all the points to the closest cluster centroid.
  • Recompute the centroids of newly formed clusters.
  • Repeat steps 3 and 4.

K-Means Clustering is a method of dividing data into groups or clusters. It works by assigning each point to the cluster it most resembles in terms of distance. The algorithm iteratively assigns new cluster centers to unassigned points until all points are assigned to a cluster and no further changes in clustering are required.

This process can be done manually or with a machine learning algorithm. Some of the steps involved are:

  • Determine the number of clusters
  • Select a clustering method
  • Group similar data together
  • Calculate dissimilarities and similarities between groups
  • Create an output table with cluster information and values for each value in the input table

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Course Content

  1. Introduction to Machine Learning
    1m 3s
  2. Environment Setup part 1
    3m 26s
  3. Environment Setup part 2
    4m 43s
  4. Environment Setup part 3
    2m 33s

  1. Data Wrangling
    12m 26s
  2. Importing Libraries and Dataset
    11m 19s
  3. Handling Missing Data
    11m 51s
  4. Handling Missing Data - Practical
    17m 3s
  5. Encoding Categorical Data
    6m 34s
  6. Encoding Categorical Data - Practical
    8m 3s
  7. Splitting Dataset
    3m 38s
  8. Splitting Dataset - Practical
    9m 36s
  9. Normalizing the Data - Part 1
    6m 18s
  10. Normalizing the Data - Part2
    6m 58s
  11. Finding Machine Learning Datasets
    9m 17s
  12. Exploratory Data Analysis
    9m
  13. Plotting Graphs - Part 1
    9m
  14. Plotting Graphs - Part 2
    23m 44s
  15. Distribution Models - Part 1
    33m 54s
  16. Distribution Models - Part 2
    13m 56s
  17. Assignment of Data Preprocessing for Machine Learning
    1m 39s

  1. Machine Learning Paradigms
    9m 53s
  2. Sampling Methods
    19m 43s
  3. Underfitting and Overfitting in Models
    6m 7s
  4. Variance and Bias
    9m 49s

  1. Distance Metrics
    6m 2s
  2. K-Means Clustering
    11m 3s
  3. K-Means Clustering - Practical
    9m 49s
  4. Hierarchical Clustering - Agglomerative , Divisive
    4m 38s
  5. Agglomerative Clustering - Practical
    4m 44s
  6. Divisive Clustering - Practical
    14m 42s
  7. DBscan Spatial Clustering
    8m 49s
  8. FP Growth
    7m 43s
  9. Assignment of Unsupervised Learning Algorithms
    2m 41s

  1. Overview of Dimensionality Reduction
    4m 25s
  2. Principal Component Analysis
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  3. Princinpal Component Analysis - Practical
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  4. Linear Discriminant Analysis
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  5. Linear Discriminant Analysis - Practical
    6m 42s
  6. Assignment of Dimensionality Reduction
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  1. Advance Trends in Machine Learning
    10m 13s

  1. Course Summary
    3m 51s

  1. Interview Questions part 1
    19m 22s
  2. Interview Questions part 2
    12m 23s
  3. Interview Questions part 3
    15m 20s

  1. Career Guidelines
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