Mining Points-of-Interest Association Rules from Geo-tagged Photos

Author

Ickjai Lee ; Guochen Cai ; Kyungmi Lee

Author_Institution

Sch. of Bus. (IT), James Cook Univ., Cairns, QLD, Australia

fYear

2013

Firstpage

1580

Lastpage

1588

Abstract

The advent of photo-sharing services results in massive user-generated geo-tagged photos. These photos implicitly and explicitly indicate points-of-interest and their associations. This study aims to combine two data mining techniques: clustering and association rules mining to mine areas of attraction, and their associative patterns. We analyze photos from Flickr in the area of Queensland, Australia, a popular tourist destination hosting the Great Barrier Reef and tropical rain forest. We report interesting experimental results and discuss findings.

Keywords

data mining; pattern clustering; social networking (online); Australia; Flickr; Great Barrier Reef; Queensland; associative patterns; attraction area mining; clustering; data mining techniques; photo-sharing services; points-of-interest association rules mining; tropical rain forest; user-generated geo-tagged photos; Association rules; Australia; Business; Educational institutions; Itemsets; Noise; Flickr mining; Point of interest mining; geo-tagged photos;

fLanguage

English

Publisher

ieee

Conference_Titel

System Sciences (HICSS), 2013 46th Hawaii International Conference on

Conference_Location

Wailea, Maui, HI

ISSN

1530-1605

Print_ISBN

978-1-4673-5933-7

Electronic_ISBN

1530-1605

Type

conf

DOI

10.1109/HICSS.2013.401

Filename

6480030