Novel applications of Association Rule Mining- Data Stream Mining
aut.embargo | No | en |
aut.thirdpc.contains | No | |
aut.thirdpc.permission | No | |
aut.thirdpc.removed | No | |
dc.contributor.advisor | Pears, Russel | |
dc.contributor.author | Vithal Kadam, Omkar | |
dc.date.accessioned | 2010-03-12T01:29:08Z | |
dc.date.available | 2010-03-12T01:29:08Z | |
dc.date.copyright | 2009 | |
dc.date.issued | 2009 | |
dc.date.updated | 2010-03-12T01:17:16Z | |
dc.description.abstract | From the advent of association rule mining, it has become one of the most researched areas of data exploration schemes. In recent years, implementing association rule mining methods in extracting rules from a continuous flow of voluminous data, known as Data Stream has generated immense interest due to its emerging applications such as network-traffic analysis, sensor-network data analysis. For such typical kinds of application domains, the facility to process such enormous amount of stream data in a single pass is critical. | |
dc.identifier.uri | https://hdl.handle.net/10292/826 | |
dc.language.iso | en | en |
dc.publisher | Auckland University of Technology | |
dc.rights.accessrights | OpenAccess | |
dc.subject | Data stream mining | |
dc.subject | Association rule mining | |
dc.title | Novel applications of Association Rule Mining- Data Stream Mining | |
dc.type | Thesis | |
thesis.degree.grantor | Auckland University of Technology | |
thesis.degree.level | Masters Theses | |
thesis.degree.name | Master of Computer and Information Sciences |