Exploratory pattern mining on social media using geo-references and social tagging information

This paper presents exploratory pattern mining techniques for describing communities of resources (e.g., images) and for characterising locations of interest. We utilise tagging information and collaborative geo-reference annotations for characterising resources locations by a set of descriptive patterns. The methods are embedded into an interactive approach for mining, browsing and visualising a set of patterns. As an exemplary use case, we focus on the social photo sharing application Flickr. Utilising publicly available real-world data from this platform, we provide a structural evaluation of the automatic approach as well as an exemplary case study for demonstrating the effectiveness and validity of the interactive approach.

[1]  Frank Puppe,et al.  Towards Meta-Engineering for Semantic Wikis , 2010, SemWiki@ESWC.

[2]  Stefan Wrobel,et al.  An Algorithm for Multi-relational Discovery of Subgroups , 1997, PKDD.

[3]  V. Carchiolo,et al.  Extending the definition of modularity to directed graphs with overlapping communities , 2008, 0801.1647.

[4]  Frank Puppe,et al.  A case-based approach for characterization and analysis of subgroup patterns , 2008, Applied Intelligence.

[5]  Jan Ramon,et al.  Efficient frequent connected subgraph mining in graphs of bounded tree-width , 2010, LWA.

[6]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  Stefanie N. Lindstaedt,et al.  Recommending Tags for Pictures Based on Text, Visual Content and User Context , 2008, 2008 Third International Conference on Internet and Web Applications and Services.

[8]  Frank Puppe,et al.  Semi-Automatic Visual Subgroup Mining using VIKAMINE , 2005, J. Univers. Comput. Sci..

[9]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD '00.

[10]  Willi Klösgen,et al.  Explora: A Multipattern and Multistrategy Discovery Assistant , 1996, Advances in Knowledge Discovery and Data Mining.

[11]  Frank Puppe,et al.  Exploiting Background Knowledge for Knowledge-Intensive Subgroup Discovery , 2005, IJCAI.

[12]  Wojciech Szpankowski,et al.  Assessing Significance of Connectivity and Conservation in Protein Interaction Networks , 2006, RECOMB.

[13]  Mark Newman,et al.  Detecting community structure in networks , 2004 .

[14]  Zheng Liu,et al.  A Survey on Social Image Mining , 2011, ICIC 2011.

[15]  Steve Gregory,et al.  Finding Overlapping Communities Using Disjoint Community Detection Algorithms , 2009, CompleNet.

[16]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[17]  Jiawei Han,et al.  Mining Knowledge in Geographical , 1998 .

[18]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[19]  S. Holm A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

[20]  Dimitrios Gunopulos,et al.  Constraint-Based Rule Mining in Large, Dense Databases , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[21]  Jiawei Han,et al.  Geographical topic discovery and comparison , 2011, WWW.

[22]  Michelangelo Ceci,et al.  Time-Slice Density Estimation for Semantic-Based Tourist Destination Suggestion , 2010, ECAI.

[23]  Mor Naaman,et al.  Generating diverse and representative image search results for landmarks , 2008, WWW.

[24]  Frank Puppe,et al.  Data Mining, Validation, and Collaborative Knowledge Capture , 2012, Collaboration and the Semantic Web.

[25]  Jiawei Han,et al.  Frequent pattern mining: current status and future directions , 2007, Data Mining and Knowledge Discovery.

[26]  Florian Lemmerich,et al.  Fast Discovery of Relevant Subgroup Patterns , 2010, FLAIRS Conference.

[27]  Peter A. Flach,et al.  Subgroup Discovery with CN2-SD , 2004, J. Mach. Learn. Res..

[28]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[29]  Neil J. Hurley,et al.  Detecting Highly Overlapping Communities with Model-Based Overlapping Seed Expansion , 2010, 2010 International Conference on Advances in Social Networks Analysis and Mining.

[30]  Charu C. Aggarwal,et al.  Graph Clustering , 2010, Encyclopedia of Machine Learning and Data Mining.

[31]  Stefan Wrobel,et al.  Listing closed sets of strongly accessible set systems with applications to data , 2010, LWA.

[32]  Frank Puppe,et al.  SD-Map - A Fast Algorithm for Exhaustive Subgroup Discovery , 2006, PKDD.

[33]  Ben Shneiderman,et al.  The eyes have it: a task by data type taxonomy for information visualizations , 1996, Proceedings 1996 IEEE Symposium on Visual Languages.

[34]  Stefan Wrobel,et al.  Efficient Closed Pattern Mining in Strongly Accessible Set Systems , 2007, MLG.

[35]  Geoffrey I. Webb,et al.  Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining , 2009, J. Mach. Learn. Res..

[36]  Howard J. Hamilton,et al.  Interestingness measures for data mining: A survey , 2006, CSUR.

[37]  Martin Atzmüller,et al.  Efficient Descriptive Community Mining , 2011, FLAIRS.

[38]  Florian Lemmerich,et al.  Modeling Location-Based Profiles of Social Image Media Using Explorative Pattern Mining , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[39]  Ciro Cattuto,et al.  Semantic Grounding of Tag Relatedness in Social Bookmarking Systems , 2008, SEMWEB.

[40]  C.J.H. Mann,et al.  Handbook of Data Mining and Knowledge Discovery , 2004 .

[41]  Steffen Staab,et al.  Exploiting Flickr Tags and Groups for Finding Landmark Photos , 2009, ECIR.

[42]  Roelof van Zwol,et al.  Flickr tag recommendation based on collective knowledge , 2008, WWW.

[43]  Michelangelo Ceci,et al.  Discovery of spatial association rules in geo-referenced census data: A relational mining approach , 2003, Intell. Data Anal..

[44]  Jan Ramon,et al.  Frequent subgraph mining in outerplanar graphs , 2006, KDD.

[45]  Gerd Stumme,et al.  Efficient Mining of Association Rules Based on Formal Concept Analysis , 2005, Formal Concept Analysis.

[46]  Florian Lemmerich,et al.  Fast Subgroup Discovery for Continuous Target Concepts , 2009, ISMIS.

[47]  Florian Lemmerich,et al.  VIKAMINE - Open-Source Subgroup Discovery, Pattern Mining, and Analytics , 2012, ECML/PKDD.

[48]  Ciro Cattuto,et al.  Evaluating similarity measures for emergent semantics of social tagging , 2009, WWW '09.

[49]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..