Bio-inspired constrained clustering: A case study on aspect-based sentiment analysis
| dc.contributor.author | Qasem, Mohammed | |
| 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.contributor.supervisor | Thulasiraman, Parimala (Computer Science) | en_US |
| 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.degree.discipline | Computer Science | en_US |
| dc.degree.level | Doctor of Philosophy (Ph.D.) | en_US |
| 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.description.note | October 2018 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/33133 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | 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.type | doctoral thesis | en_US |