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How to Implement K-means Clustering using Mapreduce

Implementing K-means Clustering Using MapReduce

Steps for Implementing K-means Clustering Using MapReduce

  • Description:
    The K-means clustering algorithm groups data points into K clusters based on their similarity. This process is implemented using MapReduce, where each MapReduce job is used to assign data points to the nearest centroid and recalculate the centroids iteratively until convergence.

Steps for Implementing K-means Clustering Using MapReduce

    Step 1: Mapper Function

  • Input: <k1, v1>
    k1: Line number (or other identifier for the data point)
    v1: Point (coordinates)
  • Task: For each data point, find the nearest centroid (center):
    For each center, calculate the distance from the data point to the center.
    Find the center with the minimum distance to the point.
  • Output: <k2, v2>
    k2: Nearest center (centroid index)
    v2: Data point (coordinates)

    Step 2: Reducer Function

  • Input: <k2, List<v2>>
    List of data points assigned to the centroid.
  • Task: Calculate the new cluster center (centroid) by computing the mean value of all the points assigned to this centroid.
  • Output: <k3, v3>
    k3: New center point (updated centroid)
    v3: Points that belong to this centroid
  • This process repeats until the clusters converge (centroids no longer change).

Screenshots
  • K-means Clustering Algorithm Screenshot 1
  • K-means Clustering Algorithm Screenshot 1

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