Osmar R. Zaïne Invited talk
Goeff Webb Invited talk

Special issue in the Journal of Intelligent Information Systems

PAKDD Gold Coast Australia

Invited speakers

Prof. Geoff Webb (Monash University, Australia) will give an invited talk on Four principles for finding the most interesting associations.

Prof Osmar R. Zaïane (University of Alberta, Canada) will give an invited talk on Communities Validity: Methodical Evaluation of Community Mining Algorithms.

Go to Goeff Webb invited talk. Go to Osmar R. Zaïne invited talk.


Geoff Webb
Four principles for finding the most interesting associations

Abstract: Association discovery is one of the key forms of data mining analysis but, after 20 years of intensive research by the community, many still believe that it typically finds far more associations than a user can usefully manage and that the interesting associations are usually buried in a mountain of dross. Geoff presents four simple principles that can be used to sift the probable gems from the probable dross -

1) a conjunction of terms is unlikely to be interesting if its frequency can be predicted by assuming independence between any partition thereof;

2) a conjunction of terms is unlikely to be interesting if its frequency can be predicted by the frequency of its supersets;

3) appropriate statistical testing should be applied to assess the first two principles;

and 4) if a conjunction of terms is unlikely to be interesting then any rule composed from those terms is unlikely to be interesting.

He will show how application of these principles together with top-k techniques, as supported by his state-of-the-art system Magnum Opus (http:www.giwebb.com), can often find small numbers of key associations.

Bio: Geoff Webb is a Professor of Information Technology Research in the Faculty of Information Technology at Monash University, where he heads the Centre for Research in Intelligent Systems. Prior to Monash he held appointments at Griffith University and then Deakin University, where he received a personal chair. His primary research areas are machine learning, data mining, and user modelling. He is known for his contribution to the debate about the application of Occam's razor in machine learning and for the development of numerous methods, algorithms and techniques for machine learning, data mining and user modelling. His commercial data mining software, Magnum Opus, incorporates many techniques from his association discovery research. Many of his learning algorithms are included in the widely-used Weka machine learning workbench. He is editor-in-chief of Data Mining and Knowledge Discovery, co-editor of the Springer Encyclopedia of Machine Learning, a member of the advisory board of Statistical Analysis and Data Mining and a member of the editorial boards of Machine Learning and ACM Transactions on Knowledge Discovery from Data. He was co-PC Chair of the 2010 IEEE International Conference on Data Mining and co-General Chair of the 2012 IEEE International Conference on Data Mining.

Osmar R. Zaïane
Communities Validity: Methodical Evaluation
of Community Mining Algorithms

Abstract: Grouping data points is one of the fundamental tasks in data mining, which is commonly known as clustering if data points are described by attributes. When dealing with interrelated data that is represented in the form of nodes and their relationships and the grouping is based on these relationships but not the node attributes, this task is also referred to as community mining. There has been a considerable number of approaches proposed in recent years for mining communities in a given network. However, little work has been done on how to evaluate community mining algorithms. The common practice is to evaluate the algorithms based on their performance on standard benchmarks for which we know the ground-truth. This technique is similar to external evaluation of attribute-based clustering methods. The other two well-studied clustering evaluation approaches are less explored in the community mining context; internal evaluation to statistically validate the clustering result, and relative evaluation to compare alternative clustering results. These two approaches enable us to validate communities discovered in a real world application, where the true community structure is hidden in the data. In this talk, we investigate different clustering quality criteria applied for relative and internal evaluation of clustering data points with attributes, and also different clustering agreement measures used for external evaluation; and incorporate proper adaptations to make them applicable in the context of interrelated data in complex networks. We further compare the performance of the proposed adapted criteria in evaluating community mining results in different settings through extensive set of experiments.

Bio: Osmar R. Zaïane is a Professor in Computing Science at the University of Alberta, Canada, and Scientific Director of the Alberta Innovates Centre for Machinre Learning (AICML). Dr. Zaiane joined the University of Alberta in July of 1999. He obtained a Master's degree in Electronics at the University of Paris, France, in 1989 and a Master's degree in Computer Science at Laval University, Canada, in 1992. He obtained his Ph.D. from Simon Fraser University, Canada, in 1999 under the supervision of Dr. Jiawei Han. His Ph.D. thesis work focused on web mining and multimedia data mining. He has research interests in novel data mining algorithms, web mining, text mining, image mining, social network analysis, and information retrieval. He has published more than 150 papers in refereed international conferences and journals, and taught on all six continents. Osmar Zaiane is the Secretary-Treasurer of the ACM SIGKDD (Special Interest Group on Data Mining) and was the treasurer of ACM SIGHIT (Special Interest group on Health Informatics). He was the Associate Editor then Editor-inChief of the ACM SIGKDD Explorations from 2003 to 2010. He is also Associate Editor of the Knowledge and Information Systems, An International Journal, by Springer, and of the journal Data Mining and Knowledge Discovery by Springer, as well as the International Journal of Internet Technology and Secured Transactions He was the General co-Chair of the IEEE International Conference on Data Mining ICDM 2011. Osmar Zaiane received the ICDM Outstanding Service Award in 2009 and the 2010 ACM SIGKDD Service Award.

Go to Goeff Webb invited talk. Go to Osmar R. Zaïne invited talk.