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Published in:
2013 | OriginalPaper | Chapter
Mining Outlier Participants: Insights Using Directional Distributions in Latent Models
Authors : Didi Surian, Sanjay Chawla
Published in: Machine Learning and Knowledge Discovery in Databases
Publisher: Springer Berlin Heidelberg
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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
M
ining
O
utlier
P
articipants from
P
rojects (
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.