Clustering With Spark

Konur Unyelioglu shows how to implement k-means and Guassian clustering techniques in Apache Spark using MLlib:

Clustering is the task of assigning entities into groups based on similarities among those entities. The goal is to construct clusters in such a way that entities in one cluster are more closely related, i.e. similar to each other than entities in other clusters. As opposed to classification problems where the goal is to learn based on examples, clustering involves learning based on observation. For this reason, it is a form of unsupervised learning task.

There are many different clustering algorithms and a central notion in all of those is the definition of ’similarity’ between the entities that are being grouped. Different clustering algorithms may have different ways of measuring the similarity. In many clustering algorithms, another common notion is the so-called cluster center, which is a basis to represent the cluster. For example, in K-means clustering algorithm, the cluster center is the arithmetic mean position of all the points in that cluster.

This is a fairly lengthy article but if you want to get into machine learning with Spark, it’s a good one.

Related Posts

Last-Click Attribution With Databricks Delta

Caryl Yuhas and Denny Lee give us an example of building a last-click digital marketing attribution model with Databricks Delta: The first thing we will need to do is to establish the impression and conversion data streams.   The impression data stream provides us a real-time view of the attributes associated with those customers who were served the […]

Read More

Getting Started With Azure Databricks

David Peter Hansen has a quick walkthrough of Azure Databricks: RUN MACHINE LEARNING JOBS ON A SINGLE NODE A Databricks cluster has one driver node and one or more worker nodes. The Databricks runtime includes common used Python libraries, such as scikit-learn. However, they do not distribute their algorithms. Running a ML job only on the driver might not […]

Read More


July 2016
« Jun Aug »