@inproceedings{abb48f176d5b481b865744a9eaf5698e,
title = "Mining outlier participants: Insights using directional distributions in latent models",
abstract = "In this paper we will propose a new probabilistic topic model to score the expertise of participants on the projects that they contribute to based on their previous experience. Based on each participant's score, we rank participants and define those who have the lowest scores as outlier participants. Since the focus of our study is on outliers, we name the model as Mining Outlier Participants from Projects (MOPP) model. MOPP is a topic model that is based on directional distributions which are particularly suitable for outlier detection in high-dimensional spaces. Extensive experiments on both synthetic and real data sets have shown that MOPP gives better results on both topic modeling and outlier detection tasks.",
author = "Didi Surian and Sanjay Chawla",
year = "2013",
doi = "10.1007/978-3-642-40994-3\_22",
language = "English",
isbn = "9783642409936",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
number = "PART 3",
pages = "337--352",
booktitle = "Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Proceedings",
address = "Germany",
edition = "PART 3",
note = "13th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2013 ; Conference date: 23-09-2013 Through 27-09-2013",
}