An Overview Of Data Clustering Techniques Provided by Apache Spark

Apache Spark is hailed for its exceptional data processing and analyzing capacities that are a result of its well-developed machine learning library (MLib).

Data clustering is typically an offline process that groups several entities from the dataset based on set criteria for a particular cluster. Rather than example based learning that happens in data classification, data clustering relies on observational learning and can be automated easily as it does not require supervision. Apache Spark’s machine learning capabilities are thus suited for such tasks.

Clustering algorithms work on identifying the similarity between each entity while assigning them to appropriate clusters. Most of them have a defined cluster center, which forms the basis of cluster representation.

The statistical outputs of data clustering are used across verticals (medicine, biology, social sciences, networking, businesses, computer technology, etc.). Following are the clustering techniques that are widely used:

• Clustering entities based on a certain feature and analyzing the closeness of each entity to the cluster center (perfect match).

• Clustering entities based on a set of criteria and calculating the cluster center. The cluster center can then be used to represent all entities in the said cluster.

• Clustering a huge set of data and determining the cut-off values for certain entity features that fall into the cluster. These values can then be used to form other clusters using the previously analyzed feature values as the clustering criteria.

• Clustering can be used to classify a data set as a whole, based on the cluster center of the entire data set.

Although there are several custom clustering algorithms written for data clustering using Apache Spark, there are 3 main algorithms used as default.

• K-means

MLib provides a default implementation for this algorithm. The number of clusters (k) is defined at the onset, and the data set’s cluster centers and data points are adjusted through an iterative algorithm. A cost minimization function WCSS (Within Cluster Sum of Squares) comes into play here. When the process is over, k clusters are obtained.

• Bisecting K-means

This is a variation of K-means algorithm. The number of clusters (k) is to be defined and the divisive hierarchical algorithm groups the data set entities into the given number of clusters. Apache Spark’s MLib provides an implementation for the bisecting K-means as well.

• Gaussian mixture

This algorithm is modeled on the Gaussian Mixture Model of data analysis. The Gaussian mixture algorithm is a type of expectation maximization algorithm. The default implementation of this is provided by Spark MLib. It also requires a predefined number of clusters at the onset.

As mentioned above, there are several other clustering techniques that do not require a predefined number of clusters. However, in the default Apache Spark data clustering algorithms, it is necessary to determine the value of k such that the grouping takes place optimally. A popular method is the Elbow method, wherein a low value is first assigned to k, and is iteratively increased while logging the values of the cost function (WCSS). At a certain point, the WCSS drops too low on a slight increase in k and then recovers marginally with each increment of k. The value just after the sudden low drop is the optimal value of k- the number of predefined clusters, which will give near accurate clusters for further analysis.

The iterative implementations of each of the three algorithms provide different results, and hence, the results should take into account the WCSS value to determine efficiency of clustering. The implementation times of the data clustering algorithms are dependent on the type of Apache Spark installation.

The Apache Spark MLib provides an API for implementing these algorithms. It also provides three other algorithms viz. Power iteration clustering, Latent Dirichlet allocation and Streaming K-means.

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