P-FCM: A proximity-based fuzzy clustering for user-centered web applications. In last years, the Internet and the web have been evolved in an astonishing way. Standard web search services play an important role as useful tools for the Internet community even though they suffer from a certain difficulty. The web continues its growth, making the reliability of Internet-based information and retrieval systems more complex. Nevertheless there has been a substantial analysis of the gap between the expected information and the returned information, the work of web search engine is still very hard. There are different problems concerning web searching activity, one among these falls in the query phase. Each engine provide an interface which the user is forced to learn. Often, the searching process returns a huge list of answers that are irrelevant, unavailable, or outdated. The tediosity of querying, due to the fact the queries are too weak to cope with the user’s expressiveness, has stimulated the designers to enrich the human-system interaction with new searching metaphors. One of these is the searching of “similar” pages, as offered by Google, Yahoo and others. The idea is very good, since the similarity gives an easy and intuitive mechanism to express a complex relation. We believe that this approach could become more effective if the user can rely on major flexibility in expressing the similarity dependencies with respect the current and available possibilities. In this paper we introduce a novel method for considering and processing the user-driven similarity during web navigation. We define an extension of fuzzy C-means algorithm, namely proximity fuzzy C-means (P-FCM) incorporating a measure of similarity or dissimilarity as user’s feedback on the clusters. We present the theoretical framework of this extension and then we observe, through a suite of web-based experiments, how significant is the impact of user’s feedback during P-FCM functioning. These observations suggest that the P-FCM approach can offer a relatively simple way of improving the web page classification according with the user interaction with the search engine.

References in zbMATH (referenced in 16 articles , 1 standard article )

Showing results 1 to 16 of 16.
Sorted by year (citations)

  1. Liu, Shihu; Chen, Xiaozhou; Moughal, Tauqir Ahmed; Yu, Fusheng: Fuzzy collaborative clustering-based ranking approach for complex objects (2015)
  2. Antoine, V.; Quost, B.; Masson, M.-H.; Denoeux, T.: CEVCLUS: evidential clustering with instance-level constraints for relational data (2014) ioport
  3. Di Martino, Ferdinando; Sessa, Salvatore; Barillari, Umberto E. S.; Barillari, Maria Rosaria: Spatio-temporal hotspots and application on a disease analysis case via GIS (2014) ioport
  4. Wei, Jia; Zeng, Qun-fang; Wang, Xuan; Wang, Jia-bing; Wen, Gui-hua: Integrating local and global topological structures for semi-supervised dimensionality reduction (2014) ioport
  5. Gacek, Adam; Pedrycz, Witold: Description, analysis, and classification of biomedical signals: a computational intelligence approach (2013) ioport
  6. Yan, Yang; Chen, Lihui; Tjhi, William-Chandra: Fuzzy semi-supervised co-clustering for text documents (2013) ioport
  7. Graves, Daniel; Noppen, Joost; Pedrycz, Witold: Clustering with proximity knowledge and relational knowledge (2012) ioport
  8. Mitra, Sushmita; Kundu, Partha Pratim; Pedrycz, Witold: Feature selection using structural similarity (2012) ioport
  9. Braak, Cajo J. F. Ter; Kourmpetis, Yiannis; Kiers, Henk A. L.; Bink, Marco C. A. M.: Approximating a similarity matrix by a latent class model: a reappraisal of additive fuzzy clustering (2009)
  10. Dong, Yihong; Cao, Shaoka; Chen, Ken; He, Maoshun; Tai, Xiaoying: PFHC: A clustering algorithm based on data partitioning for unevenly distributed datasets (2009)
  11. Pedrycz, Witold; Rai, Partab: Collaborative clustering with the use of Fuzzy C-means and its quantification (2008)
  12. Soto, J.; Flores-Sintas, A.; Palarea-Albaladejo, J.: Improving probabilities in a fuzzy clustering partition (2008)
  13. Loia, Vincenzo; Pedrycz, Witold; Senatore, Sabrina; Sessa, Maria I.: Interactive knowledge management for agent-assisted web navigation (2007)
  14. Pedrycz, Witold: Knowledge-based clustering. From data to information granules. (2005)
  15. Pedrycz, Witold; Loia, Vincenzo; Senatore, Sabrina: P-FCM: a proximity-based fuzzy clustering (2004)
  16. Loia, V.; Pedrycz, W.; Senatore, S.: P-FCM: A proximity-based fuzzy clustering for user-centered web applications. (2003)