ODD 2013 - KDD 2013 Workshop on Outlier Detection and Description

The goal of the workshop on Outlier Detection and Description (ODD) is to address outlier mining as the twofold task of outlier detection, and outlier description. In other words, the quantitiave 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.
What's ODD?
Traditionally, outlier mining 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.
Even though outlier detection has been studied for several decades, awareness for the need of outlier descriptions has only recently raised attention in the data mining community. Mining outlier descriptions is currently being studied in different forms in contrast mining, pattern mining, data compression, graph outlier mining, subspace outlier mining, in addition to other fields including data visualization, image saliency detection, and astronomy. We strongly believe there is a significant overlap in the techniques of these different fields and that developments in either setting can have a significant impact on the other. Therefore, the goal of this workshop is to bring together researchers with a shared interest in outlier detection and outlier description methods, whether for use in traditional databases, graph databases, data streams or in the processing of other large and complex data sources.
Our aim is hence to bring these and other communities together in one venue. With ODD, our objectives are to: 1) further increase the general interest on this important topic in the broader research community; 2) bring together experts from closely related areas (e.g., outlier detection and contrast mining) to shed light on how this emerging new research direction can benefit from other well-established areas; 3) provide a venue for active researchers to exchange ideas and explore important research issues in this area. Overall, the idea behind ODD is that outlier detection and description together will provide novel techniques that assist humans in manual outlier verification by easy-to-understand descriptions, and so will help to advance the state of the art and applicability of outlier mining.
Invited Speakers
We are proud to have Charu Aggarwal and Raymond Ng as keynote speakers.
Charu Aggarwal will give a presentation titled 'Outlier Ensembles'. Charu is a Research Scientist at IBM T.J. Watson, New York. His research interests include outlier analysis, graph mining, social networks, data stream mining, and mining high dimensional data. He has published over 200 papers in refereed conferences and journals, 8 books, and has applied for or been granted over 80 patents. His h-index is 56. In January 2013 he published a monograph on Outlier Analysis.
Raymond Ng is Professor of Computer Science at the University of British Columbia, Canada. His research areas include data mining, health informatics and data bases. In recent years, he has been focusing on the analysis of genomics data and text data, including the development of biomarker panels for predicting organ failures. Amongst many contributions, he is one of the co-authors of the famous LOF outlier detection algorithm.
More details on their presentations will follow shortly.
Workshop Format
ODD will be a half-day workshop on August 11th. We aim at presenting a balanced program consisting of the following elements:
Invited talks (two, 30 minutes each)
Regular papers (approx. 20 minutes, including questions)
Demonstration papers (approx. 15 minutes, including questions)
Important Dates
The following deadlines are, until further notice, preliminary.
Submission Deadline 28th of May 2013, 23:59 PST
Notification to Authors 22st of June 2013, 23:59 PST
Camera-ready Deadline 3rd of July 2013, 23:59 PST
Workshop day 11th of August 2013
Call for Papers
Topics of interests for the workshop include, but are not limited to:
interleaved detection and description of outliers
description models for given outliers
pattern and local information based outlier description
ensemble methods for outlier detection and description
statistical methods for outlier detection and description
comparative studies on outlier description
identification of outlier rules
supervised and unsupervised outlier detection
distance-based or density-based models for (local) outlier ranking
subspace outlier mining in high dimensional data
community outlier mining in graph data
anytime outlier mining in stream data
contrast mining and causality analysis
visualization techniques for presentation of and interactive detection and evaluation of outliers
human-in-the-loop modeling and learning