APPECT: An Approximate Backbone-Based Clustering Algorithm for Tags

  • Yu Zong
  • Guandong Xu
  • Ping Jin
  • Yanchun Zhang
  • EnHong Chen
  • Rong Pan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7120)


In social annotation systems, users label digital resources by using tags which are freely chosen textual descriptions. Tags are used to index, annotate and retrieve resource as an additional metadata of resource. Poor retrieval performance remains a major problem of most social tagging systems resulting from the severe difficulty of ambiguity, redundancy and less semantic nature of tags. Clustering method is a useful tool to address the aforementioned difficulties. Most of the researches on tag clustering are directly using traditional clustering algorithms such as K-means or Hierarchical Agglomerative Clustering on tagging data, which possess the inherent drawbacks, such as the sensitivity of initialization. In this paper, we instead make use of the approximate backbone of tag clustering results to find out better tag clusters. In particular, we propose an APProximate backbonE-based Clustering algorithm for Tags (APPECT).The main steps of APPECT are: (1) we execute the K-means algorithm on a tag similarity matrix for M times and collect a set of tag clustering results Z = C 1,C 2,...,C m ; (2) we form the approximate backbone of Z by executing a greedy search; (3) we fix the approximate backbone as the initial tag clustering result and then assign the rest tags into the corresponding clusters based on the similarity. Experimental results on three real world datasets namely MedWorm, MovieLens and Dmoz demonstrate the effectiveness and the superiority of the proposed method against the traditional approaches.


Approximate backbone Tag clustering Social annotation systems 


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  1. 1.
    Zong, Y., Xu, G.D., Jin, P., et al.: A local information passing clustering algorithm for tagging systems. In: The Second Workshop on Social Networks and Social Media Mining on the Web, Hong Kong, pp. 333–343 (2011)Google Scholar
  2. 2.
    Durao, F., Dolog, P.: Extending a hybrid tag-based recommender system with personalization. In: SAC 2010: Proceedings of the 2010 ACM Symposium on Applied Computing, pp. 1723–1727. ACM, New York (2010)Google Scholar
  3. 3.
    Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., Stumme, G.: Tag Recommendations in Folksonomies. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 506–514. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Tso-Sutter, K.H.L., Marinho, L.B., Schmidt-Thieme, L.: Tag-aware recommender systems by fusion of collaborative filtering algorithms. In: SAC 2008: Proceedings of the 2008 ACM Symposium on Applied Computing, pp. 1995–1999. ACM, New York (2008)Google Scholar
  5. 5.
    Gemmell, J., Shepitsen, A., Mobasher, M., Burke, R.: Personalization in folksonomies based on tag clustering. In: Proceedings of the 6th Workshop on Intelligent Techniques for Web Personalization and Recommender Systems (July 2008)Google Scholar
  6. 6.
    Shepitsen, A., Gemmell, J., Mobasher, B., Burke, R.: Personalized recommendation in social tagging systems using hierarchical clustering. In: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 259–266. ACM (2008)Google Scholar
  7. 7.
    Zong, Y., Jiang, H., Li, M.C.: Approximate backbone guided reduction clustering algorithm. Journal of Electronics and Information Technology 31(2), 2953–2957 (2009)Google Scholar
  8. 8.
    Astrain, J.J., Echarte, F., Córdoba, A., Villadangos, J.: A Tag Clustering Method to Deal with Syntactic Variations on Collaborative Social Networks. In: Gaedke, M., Grossniklaus, M., Díaz, O. (eds.) ICWE 2009. LNCS, vol. 5648, pp. 434–441. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Simpson, E.: Clustering tags in enterprise and web folksonomies. HP Labs Technical Reports, citeulike: 2545406 (2008)Google Scholar
  10. 10.
    Boratto, L., Carta, S., Vargiu, E.: RATC: A Robust Automated Tag Clustering Technique. In: Di Noia, T., Buccafurri, F. (eds.) EC-Web 2009. LNCS, vol. 5692, pp. 324–335. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Zhou, J.L., Nie, X.J., Qin, L.J., et al.: Web clustering based on tag set similarity. Journal of Computers 6(1), 59–66 (2011)Google Scholar
  12. 12.
    Matteo, N.R., Peroni, S., Tamburini, F., et al.: A parametric architecture for tags clustering in folksonomic search engines. In 9th international Conference on Intelligent Systems Design and Applications, Pisa, Italy, pp. 279–282 (2009)Google Scholar
  13. 13.
    Chen, H., Dumais, S.: Bringing order to the web: Automatically categorizing search results. In: Proceedings of the SIGCHI Conference on Human Factors in Computting Systems, pp. 145–152. ACM (2000)Google Scholar
  14. 14.
    van Dam, J., Vandic, D., Hogenboom, F., Frasincar, F.: Searching and browsing tagspaces using the semantic tag clustering search framework. In: 2010 IEEE Fourth International Conference on Semantic Computing (ICSC), pp. 436–439. IEEE (2010)Google Scholar
  15. 15.
    Lehwark, P., Risi, S., Ultsch, A.: Visualization and clustering of tagged music data. Data Analysis, Machine Learning and Applications, 673–680 (2008) Google Scholar
  16. 16.
    Miao, G., Tatemura, J., Hsiung, W., Sawires, A., Moser, L.: Extracting data records from the web using tag path clustering. In: Proceedings of the 18th International Conference on World Wide Web, pp. 981–990. ACM (2009)Google Scholar
  17. 17.
    Giannakidou, E., Koutsonikola, V., Vakali, A., Kompatsiaris, Y.: Co-clustering tags and social data sources. In: The Ninth International Conference on Web-Age Information Management, pp. 317–324. IEEE (2008)Google Scholar
  18. 18.
    Nicola, R.D., Silvio, P., Fabio, T., et al.: Of mice and terms: Clustering algorithms on ambiguous terms in folksonomies. In: Proceeding of the 2010 ACM Symposium on Applied Computing SAC 2010, pp. 844–848 (2010)Google Scholar
  19. 19.
    Jiang, Y.X., Tang, C.J., Xu, K.K., et al.: Core-tag clustering for web2.0 based on multi-similarity measurements. In: The Joint International Conference on Asia-Pacific Web Conference (APWeb) and Web-Age Information Management (WAIM), Suzhou, China, pp. 222–233 (2009)Google Scholar
  20. 20.
    Zou, P., ZHou, Z.H., Chen, G.L.: Approximate backbone guided fast ant algorithm to QAP. Journal of Software 16(10), 1691–1698 (2005)CrossRefzbMATHGoogle Scholar
  21. 21.
    Jiang, H., Zhang, X.C., Chen, G.L.: Exclusive overall optimal solution of graph bipartition problem and backbone compute complexity. Chinese Science Bulletin 52(17), 2077–2081 (2007)CrossRefGoogle Scholar
  22. 22.
    Jiang, H., Zhang, X.C., Chen, G.L.: Backbone analysis and algorithm design of QAP. Chinese Science 38(01), 1–14 (2008)Google Scholar
  23. 23.
    Guan, Z., Wang, C., Bu, J., Chen, C., Yang, K., Cai, D., He, X.: Document recommendation in social tagging services. In: Proceedings of the 19th International Conference on World Wide Web, pp. 391–400. ACM (2010)Google Scholar
  24. 24.
    Mika, P.: Ontologies Are US: A Unified Model of Social Networks and Semantics. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 522–536. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  25. 25.
    Lin, X., Guo, L., Zhao, Y.E.: Tag-bsed social interest discovery. In: Proceeding of the 17th International World Wide Web Conference (2008)Google Scholar
  26. 26.
    Sibson, R.: SLINK: An optimally efficient algorithm for single-link cluster method. Computer Journal 16(1), 30–34 (1973)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yu Zong
    • 1
    • 2
  • Guandong Xu
    • 3
  • Ping Jin
    • 1
  • Yanchun Zhang
    • 3
  • EnHong Chen
    • 2
  • Rong Pan
    • 4
  1. 1.Department of Information and EngineeringWest Anhui UniversityLuanChina
  2. 2.Department of Computer Science and TechnologyUniversity of Science and TechnologyHefeiChina
  3. 3.Center for Applied InformaticsVictoria UniversityAustralia
  4. 4.Department of Computer ScienceAalborg UniversityDenmark

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