title: Query-Based Outlier Detection in Heterogeneous Information Networks.
abstract: Outlier or anomaly detection in large data sets is a fundamental task in data science, with broad applications. However, in real data sets with high-dimensional space, most outliers are hidden in certain dimensional combinations and are relative to a user's search space and interest. It is often more effective to give power to users and allow them to specify outlier queries flexibly, and the system will then process such mining queries efficiently. In this study, we introduce the concept of query-based outlier in heterogeneous information networks, design a query language to facilitate users to specify such queries flexibly, define a good outlier measure in heterogeneous networks, and study how to process outlier queries efficiently in large data sets. Our experiments on real data sets show that following such a methodology, interesting outliers can be defined and uncovered flexibly and effectively in large heterogeneous networks.
authors.affiliation: Department of Computer Science, University of Illinois at Urbana-Champaign.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Kuck
authors.affiliation: Department of Computer Science, University of Illinois at Urbana-Champaign.
authors.forename: Honglei
authors.initials: H
authors.lastname: Zhuang
authors.affiliation: Computer Science Department, University of California at Santa Barbara.
authors.forename: Xifeng
authors.initials: X
authors.lastname: Yan
authors.affiliation: US Army Research Lab.
authors.forename: Hasan
authors.initials: H
authors.lastname: Cam
authors.affiliation: Department of Computer Science, University of Illinois at Urbana-Champaign.
authors.forename: Jiawei
authors.initials: J
authors.lastname: Han