Exploration of geo-tagged photos through data mining approaches

With the development of web technique and social network sites human now can produce information, share with others online easily. Photo-sharing website, Flickr, stores huge number of photos where people upload and share their pictures. This research proposes a framework that is used to extract associative points-of-interest patterns from geo-tagged photos in Queensland, Australia, a popular tourist destination hosting the great Barrier Reef and tropical rain forest. This framework combines two popular data mining techniques: clustering for points-of-interest detection, and association rules mining for associative points-of-interest patterns. We report interesting experimental results and discuss findings.

[1]  Tat-Seng Chua,et al.  Mining Travel Patterns from Geotagged Photos , 2012, TIST.

[2]  Changhu Wang,et al.  Photo2Trip: generating travel routes from geo-tagged photos for trip planning , 2010, ACM Multimedia.

[3]  Mor Naaman,et al.  How flickr helps us make sense of the world: context and content in community-contributed media collections , 2007, ACM Multimedia.

[4]  Ickjai Lee,et al.  Geographic Knowledge Discovery from Web 2.0 Technologies for Advance Collective Intelligence , 2011, Informatica.

[5]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[6]  Mor Naaman,et al.  Towards extracting flickr tag semantics , 2007, WWW '07.

[7]  Keiji Yanai,et al.  A Travel Planning System Based on Travel Trajectories Extracted from a Large Number of Geotagged Photos on the Web , 2013 .

[8]  Tomoharu Iwata,et al.  Travel route recommendation using geotags in photo sharing sites , 2010, CIKM.

[9]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[10]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD 2000.

[11]  Mor Naaman,et al.  Towards automatic extraction of event and place semantics from flickr tags , 2007, SIGIR.

[12]  Zhiguo Gong,et al.  Identifying points of interest by self-tuning clustering , 2011, SIGIR.

[13]  Jon M. Kleinberg,et al.  Mapping the world's photos , 2009, WWW '09.

[14]  Hans-Peter Kriegel,et al.  Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications , 1998, Data Mining and Knowledge Discovery.

[15]  Martha Larson,et al.  Personalized Landmark Recommendation Based on Geotags from Photo Sharing Sites , 2011, ICWSM.

[16]  Jian Pei,et al.  Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[17]  Slava Kisilevich,et al.  P-DBSCAN: a density based clustering algorithm for exploration and analysis of attractive areas using collections of geo-tagged photos , 2010, COM.Geo '10.

[18]  Pak Chung Wong,et al.  Visualizing association rules for text mining , 1999, Proceedings 1999 IEEE Symposium on Information Visualization (InfoVis'99).