A knowledge based system for mining association rules for video categories

Author

Rashed, Mazumder ; Renfeng Xu ; Dingju Zhu

Author_Institution

Lab. for Smart Comput. & Inf. Sci., Shenzhen Inst. of Adv. Technol., Shenzhen, China

fYear

2012

fDate

21-23 Nov. 2012

Firstpage

119

Lastpage

124

Abstract

Video data mining is a challenging research area due to interesting nature of unstructured video data. Generating association rules between items in a large video database plays a significant role in the video mining research areas. Applications of video association mining are not limited to the domains of surveillance, meetings, news broadcast, sports, video on demand (VOD), telemedicine, biomedical engineering and as well as online media collections. This paper concentrates on a knowledge-based system to generate association rules for selecting video categories using “Belief Rule Base (BRB)”. It has been shown that the system is efficient than traditional association rule mining.

Keywords

belief maintenance; data mining; knowledge based systems; video databases; association rules mining; belief rule base; biomedical engineering domain; knowledge based system; meetings domain; news broadcast domain; online media collections domain; sports domain; surveillance domain; telemedicine domain; video category; video data mining; video database; video-on-demand domain; Association rules; Cognition; Databases; Knowledge based systems; Knowledge engineering; Semantics; AHP; MCDM; eigenvector; pair wise matrix;

fLanguage

English

Publisher

ieee

Conference_Titel

ICT and Knowledge Engineering (ICT & Knowledge Engineering), 2012 10th International Conference on

Conference_Location

Bangkok

ISSN

2157-0981

Print_ISBN

978-1-4673-2316-1

Type

conf

DOI

10.1109/ICTKE.2012.6408539

Filename

6408539