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 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.
Cite
Text
Surian and Chawla. "Mining Outlier Participants: Insights Using Directional Distributions in Latent Models." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013. doi:10.1007/978-3-642-40994-3_22Markdown
[Surian and Chawla. "Mining Outlier Participants: Insights Using Directional Distributions in Latent Models." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013.](https://mlanthology.org/ecmlpkdd/2013/surian2013ecmlpkdd-mining/) doi:10.1007/978-3-642-40994-3_22BibTeX
@inproceedings{surian2013ecmlpkdd-mining,
title = {{Mining Outlier Participants: Insights Using Directional Distributions in Latent Models}},
author = {Surian, Didi and Chawla, Sanjay},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2013},
pages = {337-352},
doi = {10.1007/978-3-642-40994-3_22},
url = {https://mlanthology.org/ecmlpkdd/2013/surian2013ecmlpkdd-mining/}
}