Mining outlier participants: Insights using directional distributions in latent models

Didi Surian, Sanjay Chawla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages337-352
Number of pages16
Volume8190 LNAI
EditionPART 3
DOIs
Publication statusPublished - 2013
Externally publishedYes
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2013 - Prague, Czech Republic
Duration: 23 Sep 201327 Sep 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume8190 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

OtherEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2013
CountryCzech Republic
CityPrague
Period23/9/1327/9/13

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ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Surian, D., & Chawla, S. (2013). Mining outlier participants: Insights using directional distributions in latent models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 3 ed., Vol. 8190 LNAI, pp. 337-352). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8190 LNAI, No. PART 3). https://doi.org/10.1007/978-3-642-40994-3_22