04. K-means Demo

Part 1 - An Introduction to K-means Clustering Algorithm

Cd13639 C1 L2 DEMO 1 V2

Introduction to K-means Clustering

Overview

  • The K-means clustering algorithm is introduced using an artificial dataset.
  • This approach aids in illustrating the algorithm's functionality before applying it to real datasets.

Setup Requirements

  • Import necessary libraries: Pandas, Matplotlib, and NumPy.
  • Use standard scaler for data normalization.
  • Generate artificial data with NumPy specifying clusters using the loc and scale parameters.

Data Preparation

  • Create three clusters each with 50 data points in a two-dimensional space.
  • Concatenate datasets into a unified DataFrame.
  • Visualize the clusters using a scatter plot.

Normalization

  • Standardize the dataset to counter variances and standard deviations.
  • Use standard scalar for normalization by subtracting the mean and dividing by the standard deviation.

Cluster Selection

  • Determine the number of clusters using the elbow method:
    • Examine trade-offs between adding clusters and minimizing the Sum of Squared Errors (SSE).
    • Plot inertia values to find the "elbow" point, indicating the optimal number of clusters.
    • Apply K-means with three clusters as visually determined.

Conclusion

  • Simple dataset allows clear understanding of clustering.
  • K-means effectively identifies clusters despite the data's simplicity. Use in more complex scenarios highlights its advantages.

Part 2 - Automating the Elbow Method

Cd13639 C1 L2 DEMO 1b V2

Introduction to the Automated Elbow Method for Clustering

Overview

  • The process aims to automate cluster selection to enhance efficiency and accuracy.
  • Introducing the "find the elbow" function for systematic analysis.

Function Characteristics

  • Takes input: Sum of Squared Errors (SSE) and range of potential clusters.
  • Normalizes these inputs on a 0-1 scale.

Cluster Analysis Process

  • Draws a line between initial and final clusters to determine the "elbow" point.
  • Identifies the optimal cluster number where the maximum distance from the line to the point exists.
  • Outputs suggest three clusters as optimal after running the function.

Application in K-means Clustering

  • Set three clusters in the K-means algorithm for data analysis.
  • Ensures consistent clustering through a predefined random state.
  • Visual representation of results via scatter plots with color-coded clusters and defined centroids.

Conclusion

  • Provides a streamlined approach for determining optimal clusters.
  • Utilizes K-means to validate and visualize the clustering outcome.

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