Behavioral pattern prediction for stream-based data

Type Thesis

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Summary

Behavioral pattern prediction has many applications, ranging from consumer buying behavior analysis, web surfing prediction to network attack prediction. The traditional behavioral pattern prediction technique works mainly on a fixed dataset. But recent advances in digital technology generates a huge amount of data which contributes to data stream. Data evolves over time due to the concept drift. Stream-based classification also needs to evolve over time. Our goal is not to predict a single action/behavior, but a sequence of actions that can occur later depending on the previous actions. We call this problem “Behavioral Pattern Extrapolation”. In our research, we exploited a stream mining based technique along with markovian model, where we used an incremental and ensemble based technique for predicting a set of future actions. We have experimented using a number of benchmark datasets and shown the effectiveness of our approach.