GENERIC FRAMEWORKS FOR INTERACTIVE PERSONALIZED INTERESTING PATTERN DISCOVERY

The traditional frequent pattern mining algorithms generate an exponentially large number of patterns of which a substantial portion are not much significant for many data analysis endeavours. Due to this, the discovery of a small number of interesting patterns from the exponentially large number of frequent patterns according to a particular user's interest is an important task. Existing works on pattern

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Year

2016

Author dc:creator

• Bhuiyan, Md Mansurul Alam

Contributors dc:contributor

• Mohammad A Hasan
• Snehasis Mokhopadhyay
• Chris Clifton
• Elisa Bertino
• Jean Honorio

• Interative Mining
• MCMC Sampling
• Personalized Patterns

Repository record dc:identifier

https://docs.lib.purdue.edu/open_access_dissertations/1378

OAI identifier oai:identifier

oai:docs.lib.purdue.edu:open_access_dissertations-2594