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dc.contributor.supervisor Thulasiraman, Parimala (Computer Science) en_US
dc.contributor.author Qasem, Mohammed
dc.date.accessioned 2018-07-11T18:51:48Z
dc.date.available 2018-07-11T18:51:48Z
dc.date.issued 2018-07-04 en_US
dc.date.submitted 2018-07-04T14:46:09Z en
dc.identifier.uri http://hdl.handle.net/1993/33133
dc.description.abstract Clustering is an important problem in the era of big data. Exact algorithmic clustering approaches are not affordable for many real-world applications (RWA), requiring innovative, and approximation algorithms. Among them are bio or nature-inspired techniques such as “ant brood clustering algorithm” (ACA) inspired by how real ants brood sort their nests. ACA's mathematical model assumes a static radius of perception which is not adaptable to RWA. I address this issue by developing an adaptive clustering algorithm, called “ACA with Adaptive Radius (ACA-AR)” using kernel density estimation, a non-parametric statistical model, to measure average dissimilarity of data objects in ant’s neighborhood. I extend this algorithm to a search-based semi-supervised constrained clustering algorithm (CACA-AR) that incorporates supervisory information to guide the clustering algorithm towards solutions where constraints are minimally violated. I evaluate the accuracy of CACA-AR on benchmark datasets and provide a feasibility study on one RWA, aspect-based sentiment analysis. The F1-score results show that CACA-AR outperforms baseline techniques, multi-class logistic regression, and lexicon based approaches by 20%. en_US
dc.subject ant, constrained clustering, sentiment analysis en_US
dc.title Bio-inspired constrained clustering: A case study on aspect-based sentiment analysis en_US
dc.degree.discipline Computer Science en_US
dc.contributor.examiningcommittee Wang, Yang (Computer Science) Annakkage, Udaya (Electrical and Computer Engineering) Yang, Laurence T. (Computer Science, St. Francis Xavier University) en_US
dc.degree.level Doctor of Philosophy (Ph.D.) en_US
dc.description.note October 2018 en_US


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