Stream Mining Using Statistical Relational Learning

Abstract

Stream mining has gained popularity in recent years due to the availability of numerous data streams from sources such as social media and sensor networks. Data mining on such continuous streams possess a variety of challenges including concept drift and unbounded stream length. Traditional data mining approaches to these problems have difficulty incorporating relational domain knowledge and feature relationships, which can be used to improve the accuracy of a classifier. In this work, we model large data streams using statistical relational learning techniques for classification, in particular, we use a Markov Logic Network to capture relational features in structured data and show that this approach performs better for supervised learning than current state-of-the-art approaches. Additionally, we evaluate our approach with semi-supervised learning scenarios, where class labels are only partially available during training.