Keynote Speech: Big Data – Big Deal[details]
	Professor Nick Cercone
	Department of Computer Science and Engineering
	York University
	CSE 1003
	4700 Keele St. Toronto, Ontario, Canada, M3J 1P3
	Homepage: here |  Email: ncercone@yorku.ca

	Invited Speech: Multiple Instance Learning for Visual Categorisation[details]  Tutorial: Principle of Image Categorization[details coming soon!]
	Professor Xiangjian He
	School of Computing and Communications
	Faculty of Engineering & Information Technology
	University of Technology Sydney
	PO Box 123, Broadway NSW 2007, Australia
	Homepage: here | Email: Xiangjian.He@uts.edu.au

	Tutorial: Recursive and iterative clustering in granular hierarchical, network, and temporal datasets[details]
	Professor Pawan Ligras
	Dept. of Math and Computing Science
	Saint Mary's University
	Halifax, Nova Scotia, Canada, B3H 3C3
	Homepage: here | Email: pawan@cs.smu.ca

Tutorial

Title: Recursive and iterative clustering in granular hierarchical, network, and temporal datasets

Abstract. Clustering is one of the frequently used unsupervised data mining techniques for grouping similar objects. The proposed research program will investigate a novel iterative approach to clustering in a granular environment. An information granule represents an object. For example, a customer with certain purchasing patterns could be represented by an information granule. A granule is usually connected to other granules. For example, in a hierarchical environment, a customer granule will be connected to a number of product granules and vice versa. In a granular network, phone users are connected to other phone users. In a granular temporal environment, a daily pattern of events is connected to historical and future daily patterns. Traditionally, clustering of granules is done in isolation without any information on clustering of the connected granules. The primary theme of the proposed research is to simultaneously cluster all the granules iteratively. Each iteration will use results of previous clustering of connected granules, until a stable clustering of all the granules is achieved. In a hierarchical environment such as customers and products, it will mean that clustering of customers uses profiles of product clusters, and vice versa. For networked granules, a phone user is clustered using cluster profiles of the other connected users. In a temporal granular clustering, daily patterns will be clustered based on clustered profiles of historical and future patterns. These repeated applications of clustering are termed iterative in a hierarchy and are termed recursive in networks. The integrated meta-clustering of hierarchical, network, and temporal data is a multi-faceted project. Since clustering is unsupervised and we do not know the expected outcomes, it is important to study the quality of the resultant clustering. In addition to deriving quantitative evaluations, the notion of preference will be used to value a cluster based on how well-connected it is to more desirable objects. The iterative and recursive algorithms will be further modified for fuzzy and rough clustering, which allow an object to belong to multiple clusters. We plan to design, develop, implement, and test variations of the clustering algorithms for retail, mobile phone, engineering, and financial datasets.

Keywords: Crisp, rough, and fuzzy clustering; granular graphs; knowledge propagation; mobile call mining; cluster quality

Short Biography

Pawan Lingras is a graduate of IIT Bombay with graduate studies from University of Regina. He is currently a professor at Saint Mary’s University, Halifax and recently served as a UGC funded Scholar-in-Residence at SRTM University, Nanded and visiting professor at IIT Gandhinagar. He has authored more than 160 research papers in various international journals and conferences. He has also co-authored two textbooks, and co-edited two books and five volumes of research papers. His areas of interests include artificial intelligence, information retrieval, data mining, web intelligence, and intelligent transportation systems. He has served as the general co-chair, program co-chair, review committee chair, program committee member, and reviewer for various international conferences on artificial intelligence and data mining. He is also on editorial boards of a number of international journals.