Comparative analysis is an essential part of understanding how and why things work the way they do. Why postgraduate degree holders really earn more money than those with an undergraduate degree? What factors contribute to pre-term births? Why are some students more successful than others?
The above questions require comparison between various classes. Contrast-set mining was first proposed as a way to identify attributes that significantly differentiate between various classes (groups). While contrast-set mining has been widely applied for differentiating between different groups however, no clear picture seems to have
emerged regarding how to extract the contrast-sets that discriminate most between the classes. In this thesis we try to address the problem of finding meaningful contrast sets by applying Association Rule Mining. We report a new family of contrast-sets, and we present and compare the results of our experiments with the well known algorithm
for contrast-set mining - STUCCO.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:AEU.10048/1781 |
| Date | 06 1900 |
| Creators | Satsangi, Amit |
| Contributors | Zaiane, Osmar (Computing Science), Ray, Nilanjan (Computing Science), Miller, James (Electrical and Computer Engineering) |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 949858 bytes, application/pdf |
| Relation | A. Satsangi, O.R. Zaiane, IDEAS 2007: 114-119 |
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