H
Hanan Ayad
Researcher at University of Waterloo
Publications - 8
Citations - 541
Hanan Ayad is an academic researcher from University of Waterloo. The author has contributed to research in topics: Cluster analysis & Consensus clustering. The author has an hindex of 7, co-authored 8 publications receiving 497 citations.
Papers
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Journal ArticleDOI
Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters
Hanan Ayad,Mohamed S. Kamel +1 more
TL;DR: This paper proposes new consensus clustering algorithms with linear computational complexity in n and introduces the idea of cumulative voting as a solution for the problem of cluster label alignment, where unlike the common one-to-one voting scheme, a probabilistic mapping is computed.
Journal ArticleDOI
On voting-based consensus of cluster ensembles
Hanan Ayad,Mohamed S. Kamel +1 more
TL;DR: This paper shows that a recently introduced cumulative voting scheme is a special case corresponding to a linear regression method, and uses a randomized ensemble generation technique for extracting the consensus clustering from the aggregated ensemble representation and for estimating the number of clusters.
Book ChapterDOI
Finding natural clusters using multi-clusterer combiner based on shared nearest neighbors
Hanan Ayad,Mohamed S. Kamel +1 more
TL;DR: Preliminary experiments show promising results, and comparison with a recent study justifies the combiner's suitability to the pre-defined problem domain.
Book ChapterDOI
Topic Discovery from Text Using Aggregation of Different Clustering Methods
Hanan Ayad,Mohamed S. Kamel +1 more
TL;DR: Experimental evaluation shows that the aggregation can successfully improve the clustering quality and the topic accuracy over individual clustering techniques.
Book ChapterDOI
Cluster-Based cumulative ensembles
Hanan Ayad,Mohamed S. Kamel +1 more
TL;DR: A cluster-based cumulative representation for cluster ensembles, where cluster labels are mapped to incrementally accumulated clusters, and a matching criterion based on maximum similarity is used to generate high granularity clusterings.