Finding Succinct Representations For Clusters
| dc.contributor.author | Gupta, Aparna | en |
| dc.contributor.committeechair | Marathe, Madhav Vishnu | en |
| dc.contributor.committeemember | Vullikanti, Anil Kumar S. | en |
| dc.contributor.committeemember | Swarup, Samarth | en |
| dc.contributor.department | Computer Science | en |
| dc.date.accessioned | 2019-07-10T08:01:33Z | en |
| dc.date.available | 2019-07-10T08:01:33Z | en |
| dc.date.issued | 2019-07-09 | en |
| dc.description.abstract | Improving the explainability of results from machine learning methods has become an important research goal. In this thesis, we have studied the problem of making clusters more interpretable using a recent approach by Davidson et al., and Sambaturu et al., based on succinct representations of clusters. Given a set of objects S, a partition of S (into clusters), and a universe T of descriptors such that each element in S is associated with a subset of descriptors, the goal is to find a representative set of descriptors for each cluster such that those sets are pairwise-disjoint and the total size of all the representatives is at most a given budget. Since this problem is NP-hard in general, Sambaturu et al. have developed a suite of approximation algorithms for the problem. We also show applications to explain clusters of genomic sequences that represent different threat levels | en |
| dc.description.abstractgeneral | Improving the explainability of results from machine learning methods has become an important research goal. Clustering is a commonly used Machine Learning technique which is performed on a variety of datasets. In this thesis, we have studied the problem of making clusters more interpretable; and have tried to answer whether it is possible to explain clusters using a set of attributes which were not used while generating these clusters. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:21065 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/91388 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | clustering | en |
| dc.subject | integer programming | en |
| dc.title | Finding Succinct Representations For Clusters | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Computer Science and Applications | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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