Novel Data Mining Approach Predicting Alerts in The Telecom Industry

Predictive maintenance is one of the top trending new concepts in event prediction especially in light of the recent interests surrounding Artificial Intelligence and Machine Learning. It comes with a slew of useful tools for forecasting events. For example, one of the key difficulties facing the mobile telecom business is service unavailability. At the moment, we only have reactive procedures in place to solve service unavailability. But the time it takes to fix it is costing money. If we found the behavior that leads to service disruptions in our network, we can predict them using AI algorithms, which would drastically improve customer experience and reduce revenue loss. Data mining techniques are one of the main AI algorithms for event prediction. The purpose of this paper is to demonstrate how we can tackle this legacy challenge using intense data mining methods. The study proposes a new approach for event prediction that employs concurrency for Rare Association Rule Mining (RAR). The proposed method is a prediction with a suggested course of action to avoid consequences.