Richard Ivey Building 2340
- Data Driven Approaches
- Business Decision-Making
- Statistical Analysis
- Management Science
- Bayesian Statistics
- Operations Research
- Big Data Analytics
- Internet of Things
- Read the Impact article featuring research from Professor Kashef
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Rasha Kashef is an Assistant Professor in Management Science at the Ivey Business School. She is also cross-appointed with the Computer Science Department at Western University. Rasha's research interests focus on the application of machine learning algorithms and tools in Big-data Analytics, Social media, operations research, and bioinformatics.
- Decision Making with Analytics (HBA Core)
- Simulation and Risk Analysis (MSc Program)
- Big Data Analytics (MSc Program)
- Competitive Advantage (Electives)
- Ph.D., University of Waterloo, 2008
- MSc., Academy Institute for Science and Technology, 2004
- BSc., Alexandria University, 2001
- PEng, Professional engineer of Ontario
Recent Refereed Articles
Kashef, R. F.; Kamel, M. S.,
2010, "Cooperative Clustering", Pattern Recognition, June 43(6): 2315 - 2329.
Abstract: Data clustering plays an important role in many disciplines, including data mining, machine learning, bioinformatics, pattern recognition, and other fields, where there is a need to learn the inherent grouping structure of data in an unsupervised manner. There are many clustering approaches proposed in the literature with different qualitycomplexity tradeoffs. Each clustering algorithm works on its domain space with no optimum solution for all datasets of different properties, sizes, structures, and distributions. In this paper, a novel cooperative clustering (CC) model is presented. It involves cooperation among multiple clustering techniques for the goal of increasing the homogeneity of objects within the clusters. The CC model is capable of handling datasets with different properties by developing two data structures, a histogram representation of the pair-wise similarities and a cooperative contingency graph. The two data structures are designed to find the matching sub-clusters between different clusterings and to obtain the final set of clusters through a coherent merging process. The cooperative model is consistent and scalable in terms of the number of adopted clustering approaches. Experimental results show that the cooperative clustering model outperforms the individual clustering algorithms over a number of gene expression and text documents datasets.
Link(s) to publication:
Kashef, R. F.; Kamel, M. S.,
2009, "Enhanced bisecting k-means clustering using intermediate cooperation", Pattern Recognition, November 42(11): 2557 - 2569.
Abstract: Bisecting k-means (BKM) is very attractive in many applications as document-retrievalindexing and gene expression analysis problems. However, in some scenarios when a fraction of the dataset is left behind with no other way to re-cluster it again at each level of the binary tree, a refinement is needed to re-cluster the resulting solutions. Current approaches to refine the clustering solutions produced by the BKM employ end-result enhancement using k-means (KM) clustering. In this hybrid model, KM waits for the former BKM to finish its clustering and then it takes the final set of centroids as initial seeds for a better refinement. In this paper, a cooperative bisecting k-means (CBKM) clustering algorithm is presented. The CBKM concurrently combines the results of the BKM and KM at each level of the binary hierarchical tree using cooperative and merging matrices. Undertaken experimental results show that the CBKM achieves better clustering quality than that of KM, BKM, and single linkage (SL) algorithms with comparable time performance over a number of artificial, text documents, and gene expression datasets
Link(s) to publication:
Works in Progress
- Kashef, R.F., “Learning Social Networks Using Cooperative Clustering to Provide Patient’s Support Groups”, In progress.
- Kashef, R.F., Kamel, M.S.,“ Clustering-based Outliers Detection Using Cooperative Multiple Partitioning”, In progress.
Honours & Awards
- Meritorious Candidate, NSERC Industrial Research and Development Fellowship (IRDF), 2012-2014
- The University of Waterloo Doctoral Thesis Completion Award, Spring 2008
- Female Doctoral Scholarship, University of Waterloo, Winter 2008
- The Excellence in Teaching Assistantship Award, University of Waterloo, Ontario, Canada, Winter 2008
- The Volunteer Impact Award, Kitchener, Ontario, Canada, 2006
- University of Waterloo Graduate Scholarship, University of Waterloo, Faculty of Engineering (4 times)
- Faculty of Engineering Graduate Scholarship, University of Waterloo, Department of Electrical and Computer Engineering (4 times)
- Lecturer, Management Science Department, University of waterloo, 2013- 2016
- Online Instructor (MMSC-online), Management Science Department, University of waterloo, 2013-2016
- Postdoctoral-fellow, Applied Mathematics Department, University of Waterloo, 2011-2013
- Assistant Professor, Computer Science, Academy Institute, 2009-2011
- Research Associate, Microsoft Corp. 2008-2009