Analysis of Different Approaches of Parallel Block Processing for K-Means Clustering Algorithm

TL;DR

This paper compares various parallel block processing methods for the K-Means clustering algorithm to improve execution time and performance in processing high-resolution satellite images.

Contribution

It introduces and analyzes different parallel block processing approaches—row-shaped, column-shaped, and square-shaped—for K-Means clustering in satellite image classification.

Findings

Parallel approaches reduce execution time

Square-shaped block processing yields best performance

Improved efficiency over sequential K-Means

Abstract

Distributed Computation has been a recent trend in engineering research. Parallel Computation is widely used in different areas of Data Mining, Image Processing, Simulating Models, Aerodynamics and so forth. One of the major usage of Parallel Processing is widely implemented for clustering the satellite images of size more than dimension of 1000x1000 in a legacy system. This paper mainly focuses on the different approaches of parallel block processing such as row-shaped, column-shaped and square-shaped. These approaches are applied for classification problem. These approaches is applied to the K-Means clustering algorithm as this is widely used for the detection of features for high resolution orthoimagery satellite images. The different approaches are analyzed, which lead to reduction in execution time and resulted the influence of improvement in performance measurement compared to sequential K-Means Clustering algorithm.