Anomaly Detection from Call Data Records
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Automated lecture template generation in CORDRA-Based learning object repository
ICWL'11 Proceedings of the 10th international conference on Advances in Web-Based Learning
A mixed graph model for community detection
International Journal of Intelligent Information and Database Systems
LONET: An interactive search network for intelligent lecture path generation
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems
Modeling user-generated contents: an intelligent state machine for user-centric search support
Personal and Ubiquitous Computing
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An important problem in the area of homeland security is to identify suspicious entities in large datasets. Although there are methods from knowledge discovery and data mining (KDD) focusing on finding anomalies in numerical datasets, there has been little work aimed at discovering suspicious instances in large and complex semantic graphs whose nodes are richly connected with many different types of links. In this paper, we describe a novel, domain independent and unsupervised framework to identify such instances. Besides discovering suspicious instances, we believe that to complete the process, a system has to convince the users by providing understandable explanations for its findings. Therefore, in the second part of the paper we describe several explanation mechanisms to automatically generate human understandable explanations for the discovered results. To evaluate our discovery and explanation systems, we perform experiments on several different semantic graphs. The results show that our discovery system outperforms the state-of-the-art unsupervised network algorithms used to analyze the 9/11 terrorist network by a large margin. Additionally, the human study we conducted demonstrates that our explanation system, which provides natural language explanations for its findings, allowed human subjects to perform complex data analysis in a much more efficient and accurate manner.