A clustering-based data reduction for very large spatio-temporal datasets

TL;DR

This paper introduces a clustering-based data reduction method for large spatio-temporal datasets, enabling more efficient visualization and analysis by summarizing data with representative clusters.

Contribution

It proposes a novel clustering approach specifically designed for reducing the size of very large spatio-temporal datasets, facilitating easier analysis and visualization.

Findings

Preliminary results show effective data reduction.

Clustering improves visualization efficiency.

Method preserves essential data features.

Abstract

Today, huge amounts of data are being collected with spatial and temporal components from sources such as meteorological, satellite imagery etc. Efficient visualisation as well as discovery of useful knowledge from these datasets is therefore very challenging and becoming a massive economic need. Data Mining has emerged as the technology to discover hidden knowledge in very large amounts of data. Furthermore, data mining techniques could be applied to decrease the large size of raw data by retrieving its useful knowledge as representatives. As a consequence, instead of dealing with a large size of raw data, we can use these representatives to visualise or to analyse without losing important information. This paper presents a new approach based on different clustering techniques for data reduction to help analyse very large spatio-temporal data. We also present and discuss preliminary results of this approach.