An Application of Bayesian classification to Interval Encoded Temporal mining with prioritized items

TL;DR

This paper presents a novel method combining Bayesian classification with interval encoded temporal data and prioritized items to improve temporal rule mining, demonstrated on telecommunications complaints data.

Contribution

It introduces a priority-based temporal mining approach integrated with Bayesian classification, enhancing the effectiveness of temporal rule discovery.

Findings

Demonstrated feasibility on telecommunications complaints data

Improved temporal rule effectiveness through Bayesian classification

Validated the approach with experimental results

Abstract

In real life, media information has time attributes either implicitly or explicitly known as temporal data. This paper investigates the usefulness of applying Bayesian classification to an interval encoded temporal database with prioritized items. The proposed method performs temporal mining by encoding the database with weighted items which prioritizes the items according to their importance from the user perspective. Naive Bayesian classification helps in making the resulting temporal rules more effective. The proposed priority based temporal mining (PBTM) method added with classification aids in solving problems in a well informed and systematic manner. The experimental results are obtained from the complaints database of the telecommunications system, which shows the feasibility of this method of classification based temporal mining.