Time series numerical association rule mining variants in smart agriculture

TL;DR

This paper introduces a new algorithm for time series numerical association rule mining tailored for smart agriculture, demonstrating its potential through practical experiments and proposing a hardware monitoring environment.

Contribution

It presents a novel algorithmic approach for mining association rules from time series data in smart agriculture, filling a gap in existing numerical association rule mining methods.

Findings

The method successfully extracted meaningful association rules from agricultural time series data.

Practical experiments validated the effectiveness of the proposed approach.

The study suggests potential for further extension and application in smart farming systems.

Abstract

Numerical association rule mining offers a very efficient way of mining association rules, where algorithms can operate directly with categorical and numerical attributes. These methods are suitable for mining different transaction databases, where data are entered sequentially. However, little attention has been paid to the time series numerical association rule mining, which offers a new technique for extracting association rules from time series data. This paper presents a new algorithmic method for time series numerical association rule mining and its application in smart agriculture. We offer a concept of a hardware environment for monitoring plant parameters and a novel data mining method with practical experiments. The practical experiments showed the method's potential and opened the door for further extension.