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Machine Learning Algorithm K-means using Map Reduce for Big Data Analytics

Preface​

  • In this lecture, we will discuss machine learning classification algorithm k-means using mapreduce for big data analytics

Cluster Analysis (Clustering) Overview​

  • Goal: Organize similar items into groups
  • In cluster analysis, the goal is to organize similar items in given dataset into groups or clusters. By segmenting given data into clusters, we can analyze each cluster more carefully

Applications​

  • Segment customer base into groups
  • Characterize different weather patterns for a region
  • Group news articles into topics
  • Discover crime hot spots

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Cluster Analysis Summary​

  • Organize similar items into groups
  • Analyzing clusters often leads to useful insights about data
  • Clusters require analysis and interpretation

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Chossing Initial Centroids​

  • Issue:

Final clusters are sensitive to initial centroids

  • Solution:

Run k-means multiple times with different random intitial centroids, and choose best results

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K-Means summary​

  • Classic algorithm for cluster analysis
  • Simple to understand and implement and is efficient
  • Value of k must be specified
  • Final clusters are sensitive to initial centroids