# Parallel K-Means Clustering with Triangle Inequality

@inproceedings{Krohn2016ParallelKC, title={Parallel K-Means Clustering with Triangle Inequality}, author={Rachel Krohn and Christer Karlsson and Rachel Krohn}, year={2016} }

Clustering divides data objects into groups to minimize the variation within each group. This technique is widely used in data mining and other areas of computer science. K-means is a partitional clustering algorithm that produces a fixed number of clusters through an iterative process. The relative simplicity and obvious data parallelism of the K-means algorithm make it an excellent candidate for distributed-memory parallel optimization, particularly as datasets grow beyond the size of a…

## 2 Citations

### Analyzing Digital Evidence Using Parallel k-means with Triangle Inequality on Spark

- Computer Science2018 IEEE International Conference on Big Data (Big Data)
- 2018

Experimental results show that the parallel implementation of k-meansTI on Spark can be faster than the Spark ML k-Means when a data set is large, does not contain many sparse data, and is high dimensional.

### Efficient k-means Using Triangle Inequality on Spark for Cyber Security Analytics

- Computer ScienceProceedings of the ACM International Workshop on Security and Privacy Analytics - IWSPA '19
- 2019

This paper re-formulates the parallel version of Elkan's k-means with triangle inequality (k-meanTI) algorithm, implements this algorithm on Apache Spark, and uses it to classify Web attacks in different clusters and provides the speed comparison of the parallel k-MeansTI on Spark with the Spark ML k- means clustering algorithm.

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