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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Incremental Learning of Non-stationary Temporal Causal Networks for Telecommunication Domain
verfasst von : Ram Mohan, Santanu Chaudhury, Brejesh Lall
Erschienen in: Pattern Recognition and Machine Intelligence
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Abstract
In today’s competitive telecommunication industry understanding the causes that influence the revenue is of importance. In a continuously evolving business environment, the causes that influence the revenue keeps changing. To understand and quantify the effect of different factors we model it as a non-stationary temporal causal network. To handle the massive volume of data, we propose a novel framework as part of which we define rules to identify the concept drift and propose an incremental algorithm for learning non-stationary temporal causal structure from streaming data. We apply the framework on a telecommunication operator’s data and the framework detects the concept drift related to changes in revenue associated with data usage and the incremental causal network learning algorithm updates the knowledge accordingly.