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CALL FOR RESEARCH PAPERS
ODD @ KDD 2013
Workshop on Outlier Detection and Description
will be held in conjunction with KDD 2013
August 11th, 2013 in Chicago, USA

http://outlier-analytics.org/odd13kdd/
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ODD is a workshop organized in conjunction with the ACM SIGKDD Conference on Knowledge, Discovery, and Data Mining (KDD 2013).

The goal of the workshop is to address outlier mining as the twofold task of outlier detection, and outlier description. In other words, the quantitative and qualitative analysis of anomalies in data. These topics are rarely considered in unison, and literature for these tasks is spread over different research communities. The main goal of ODD is to bridge this gap and provide a venue for knowledge exchange between these different research areas for a corroborative union of quantitative and qualitative analyses for the study of outlier mining.

Traditionally, outlier mining and anomaly detection focused on the automatic detection of highly deviating objects. It has been studied for several decades in statistics, machine learning, data mining, and database systems, and led to a lot of insight as well as automated systems for the detection of outliers.

However, for today's applications to be successful, mere identification of anomalies alone is not enough. With more and more applications using outlier analysis for data exploration and knowledge discovery, the demand for manual verification and understanding of outliers is steadily increasing. Examples include applications such as health surveillance, customer segmentation, fraud analysis, or sensor monitoring, where one is particularly interested in why an object seems outlying.

Example: Consider outlier analysis in the domain of health surveillance. An outlier might be a patient that shows high deviation in specific vital signals like "heart beat rate" and "skin humidity". If this patient is only detected by a traditional algorithm, this is not sufficient in case of health surveillance: health professionals have to be able to verify the reasons for why this patient stands out in order to provide proper medical treatment accordingly. It is a major task for outlier analysis to assist in such a manual verification. Hence, outlier mining algorithms should provide additional descriptive information. These outlier descriptions should be easy to understand and should highlight the specific deviation of an outlier in contrast to regular patients.

CONFIRMED KEYNOTE SPEAKERS:
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Charu Aggarwal (IBM T. J. Watson Research Center)
Keynote: "Outlier Ensembles"

Raymond Ng (University of British Columbia)
Keynote: Title TBA

TOPICS OF INTEREST
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Topics of interests for the workshop include, but are not limited to:

* Interleaved detection and description of outliers
o Description models for given outliers
o Pattern and local information based outlier description
o Subspace outliers, feature selection, and space transformations
o Ensemble methods for anomaly detection and description
o Descriptive local outlier ranking
o Identification of outlier rules
o Contextual and community outliers
o Human-in-the-loop modeling and learning
o Visualization techniques for interactive exploration of outliers
o Comparative studies on outlier description
* Related research fields
o Contrast mining
o Change and novelty detection
o Causality analysis
o Frequent itemset mining
o Compression theory
o Subgroup mining
o Subspace learning
* Outlier mining for complex databases
o Graph data (e.g. community outliers)
o Spatio-temporal data
o Time series and sequential data
o Online processing of stream data
o Scalability to high dimensional data
* Formal outlier mining models
o Supervised, semi-supervised, and unsupervised models
o Statistical models
o Distance-based models
o Density-based models
o Spectral models
o Constraint-based models
o Compression-based models
o Ensemble models
* Applications of outlier detection and description
o Fraud in financial data
o Intrusions in communication networks
o Sensor network analysis
o Social network analysis
o Health surveillance
o Customer profiling
o ... and many more ...

We encourage submissions describing innovative work in other, related, fields that address the issues of outlierness in data mining.


SUBMISSION GUIDELINES
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We invite submission of unpublished original research papers that are not under review elsewhere. All papers will be peer reviewed. Full research papers may be up to 9 pages long. We also invite vision papers, descriptions of work-in-progress, case studies on benchmark data, and demonstration papers for outlier mining systems as shorter paper submissions.

Contributions should be submitted in PDF format using the workshop’s EasyChair submission site at http://www.easychair.org/conferences/?conf=odd13kdd The submitted papers must be written in English and formatted according to the standard double-column ACM Proceedings Style. Additional information about formatting is available online at: http://www.acm.org/sigs/publications/proceedings-templates [Tighter Alternate style]

For accepted papers, at least one author must attend the workshop to present the work. Accepted papers will be included in the ACM SIGKDD 2013 Digital Proceedings, as well as made available in the ACM Digital Library.

If you are considering submitting to the workshop and have questions regarding the workshop scope or need further information, please do not hesitate to contact the organizers.


IMPORTANT DATES
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Submission deadline: 4th of June 2013, 23:59 PST (extended)
Acceptance notification: 22nd of June 2013, 23:59 PST
Camera-ready deadline: 3rd of July 2013, 23:59 PST
Workshop day: 11th of August 2013



PROGRAM COMMITTEE (so far)
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Fabrizio Angiulli (University of Calabria)
Ira Assent (Aarhus University)
James Bailey (University of Melbourne)
Arindam Banerjee (University of Minnesota)
Albert Bifet (Yahoo! Labs Barcelona)
Christian Böhm (Ludwig-Maximilians-Universität München)
Rajmonda Caceres (MIT Lincoln Laboratory)
Varun Chandola (Oak Ridge National Laboratory)
Polo Chau (Georgia Tech)
Sanjay Chawla (University of Syndey)
Tijl De Bie (University of Bristol)
Christos Faloutsos (Carnegie Mellon University)
Jing Gao (University of Buffalo)
Jaakko Holmén (Aalto University)
Eamonn Keogh (University of California, Riverside)
Matthijs van Leeuwen (KU Leuven)
Daniel B. Neill (Carnegie Mellon University)
Naren Ramakrishnan (Virginia Tech)
Spiros Papadimitriou (Rutgers University)
Hanghang Tong (CUNY)
Ye Wang (Ohio State University)
Arthur Zimek (Ludwig-Maximilians-Universität München)


ORGANIZERS
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Leman Akoglu (Stony Brook University)
Emmanuel Müller (Karlsruhe Institute of Technology)
Jilles Vreeken (Universiteit Antwerpen)

You can contact us at:
odd13kdd (at) gmail.com