Streaming a k-means clustering algorithm using Spark

The k-means algorithm is an unsupervised machine learning (ML) clustering algorithm. The objective of this algorithm is to build k centers around which data points are centered, thereby forming k clusters. The most common implementation of this algorithm is generally done using batch-oriented processing. Streaming-based clustering algorithms are also available for this, with the following properties:

• The k clusters are built using initial data
• As new data arrives in minibatches, existing k clusters are updated to compute new k clusters
• It also possible to control the decay or decrease in the significance of older data

At a high level, the preceding steps are quite similar to the word count problem that we solved using the streaming solution. The goal of the k-means algorithm is to partition the data into k clusters. If the...