A scalable system for executing and scoring K-means clustering techniques and its impact on applications in agriculture. (6th June 2019)

• Record Type: Journal Article
• Authors: Golubovic, Nevena; Krintz, Chandra; Wolski, Rich; Sethuramasamyraja, Balaji; Liu, Bo
• Is Part Of: International journal of big data intelligence. Volume 6:Number 3/4(2019)
• Page Start: 163
• Page End: 175
• Publication Date: 2019-06-06
• Subjects: K-means clustering -- cloud computing; Big data -- Periodicals; 005.705
• Languages: English
• ISSNs: 2053-1389

Abstract

We present Centaurus - a scalable, open source, clustering service for K-means clustering of correlated, multidimensional data. Centaurus provides users with automatic deployment via public or private cloud resources, model selection (using Bayesian information criterion), and data visualisation. We apply Centaurus to a real-world, agricultural analytics application and compare its results to the industry standard clustering approach. The application uses soil electrical conductivity (EC) measurements, GPS coordinates, and elevation data from a field to produce a 'map' of differing soil zones (so that management can be specialised for each). We use Centaurus and these datasets to empirically evaluate the impact of considering multiple K-means variants and large numbers of experiments. We show that Centaurus yields more consistent and useful clusterings than the competitive approach for use in zone-based soil decision-support applications where a 'hard' decision is required.