• Nu S-Au Găsit Rezultate

Concluzii ¸si direct¸ii de cercetare

Ca direct¸ie de cercetare viitoare ne propunem ajustarea parametrilor ˆın fi¸sierele de configurare ale CIRCOS: ajustarea ordinii de afi¸sare a benzilor, ajustarea transparent¸ei benzilor ˆın funct¸ie de distribut¸ia valorilor ˆın datele de intrare, ascunderea sau ¸stergerea unor benzi care nu corespund unor condit¸ii date, scalarea valorilor din celule.

ˆIn plus, pl˘anuim s˘a alegem ¸si alte formate de reprezentare grafic˘a ˆın afar˘a de cel circular.

O alt˘a problem˘a pe care am vrea sa o abord˘am ˆın cercet˘arile viitoare este num˘arul mare de concepte generate de Trias. Pentru a rezolva aceast˘a problem˘a propunem construirea unui algoritm bazat pe probabilit˘at¸i condit¸ionale pentru a ¸sterge conceptele cele mai put¸in importante.

De asemenea, componenta temporal˘a este foarte important˘a ˆın log-urile web. Ne propunem s˘a folosim Analiza Conceptual˘a Formal˘a Temporal˘a pentru a studia com- portamentul utilizatorilor, atˆat individual cˆat ¸si pe grupuri.


[1] Apache Lucene,A high-performance, full-featured text search engine library, http://lucene.apache.org/

[2] Apache HTTP Server,

http://httpd.apache.org/docs/current/mod/ mod alias.html

[3] L. Becchetti, C. Castillo, D. Donato, Link-Based Characterization and Detection of web Spam, 2nd International Workshop on Adversarial Information Retrieval on the web, AIRweb, Seattle, USA, August 2006, pp. 1-8

[4] L. Becchetti, C. Castillo, D. Donato, S. Leonardi, R. Baeza-Yates, Link Analysis for web Spam Detection: Link-based and Content Based Techniques, ACM Tran- sactions on the web (Tweb), Volume 2, Issue 1, New York, USA, February 2008, pp. 1-41

[5] P. Becker, J. Hereth, G. Stumme, ToscanaJ: An Open Source Tool for Qualitative Data Analysis, Advances in Formal Concept Analysis for Knowledge Discovery in Databases., page 1-2. Lyon, France, (July 2002).

[6] B. Berelson, Content analysis in communication research, New York, Free Press, 1952

[7] T. Berners-Lee , W. Hall, J. Hendler, N. Shadbolt, D.J. Weitzner, Science 313, 769 (2006).

[8] G. Beydoun, R. Kultchitsky, G. Manasseh, Evolving semantic web with social navigation, Expert Systems with Applications, 32(2007), pp. 265–276.

[9] D. Bufnea, D. Halit¸˘a, A server-side support layer for transparent web content migration, Proceedings of the International Conference on Knowledge Engineering, Principles and Techniques, KEPT 2013 Cluj-Napoca, Studia Universitatis Babe¸s- Bolyai Informatica, 3/2013, pp. 78-89

[10] Circos, a circular visualization tool, www.circos.ca

[11] A. Dieberger, Supporting social navigation on the world wide web., International Journal of HumanComputer Studies, 46(6), 805825, 1997.

[12] Sanda Drago¸s, Diana Halit¸˘a, Christian S˘ac˘area, Diana Troanc˘a,An FCA grounded study of user dynamics through log exploration, Studia Universitatis Babes-Bolyai Series Informatica, Volume LIX, no. 2, 2014, pp. 82-97

[13] S. Drago¸s, C. S˘ac˘area, Analysing the Usage of Pulse Portal with Formal Con- cept Analysis, Studia Universitatis Babes-Bolyai Series Informatica, LVII (2012), pp. 65–75.

[14] Sanda Drago¸s, Diana Halit¸˘a, Christian S˘ac˘area, Diana Troanc˘a Applying Tria- dic FCA in Studying Web Usage Behaviors, Knowledge Science, Engineering and Management, 7th International Conference, Volume 8793, 2014, pp. 73-80

[15] S. Drago¸s, PULSE Extended, in The Fourth International Conference on Inter- net and Web Applications and Services, Venice/Mestre, Italy, May 2009, IEEE Computer Society, pp. 510–515.

[16] S. Drago¸s,Why Google Analytics can not be used for educational web content, 2011 International Conference on Next Generation Web Services Practices.

[17] Dynamic404, Logical error-pages,


[18] M. Eirinaki, M. Vazirgiannis,Web mining for web personalization, ACM Transac- tions on Internet Technology (TOIT), 3 (2003), pp. 1–27.

[19] A. Farahat, M. Bailey, How Effective is Targeted Advertising?, Proceedings of the 21st World Wide web Conference 2012, Lyon, France, April 16-20, 2012, pp.


[20] G. Gan, M. Chaoqun, W. Jianhong, Data Clustering: Theory, Algorithms, and Applications, ASA-SIAM Series on Statistics and Applied Probability, SIAM, Phi- ladelphia, Alexandria, 2007

[21] B. Ganter, R. Wille,Formal Concept Analysis. Mathematical Foundations. Sprin- ger, Berlin-Heidelberg-New York(1999).

[22] B. Goncalves, J. Ramasco,Human dynamics revealed through Web analytics, Phys.

Rev. E 78, 026123 (2008).

[23] Z. Gyongy, H. Garcia-Molina, P. Berkhin, J. Pedersen,Link Spam Detection Based on Mass Estimation, 32nd International Conference in Very Large Data Bases (VLDB), Seoul, Korea, 2006, pp. 439-450

[24] D. Halit¸˘a, D. Bufnea: A study regarding inter domain linked documents similarity and their consequent bouncerate, Studia Universitatis Babe¸s-Bolyai Informatica, 1/2014, pp. 83-91

[25] A. Huang,Similarity Measures for Text Document Clustering, Proceedings od the New Zealand Computer Science Research Student Conference, Hamilton, New Zealand, 2008, pp. 49-56

[26] R. Jaeschke, A. Hotho, C. Schmitz, B.Ganter, G. Stumme, Trias - An Algorithm for Mining Iceberg Trilattices, Proceedings of the IEEE International Conference on Data Mining, pp. 907-911, Hong Kong, IEEE Computer Society, 2006.

[27] The Joint Industry Committee for Web Standards (JICWEBS), Reporting stan- dards. website traffic.Auditing Bureau of Circulations electronic (ABCe), Report 1, 2011.

[28] R. Kosala, H. Blockeel,Web mining research: A survey, ACM Sigkdd Explorations Newsletter, 2 (2000), pp. 1–15.

[29] F. Lehmann, R. Wille,A Triadic Approach to Formal Concept Analysis, Concep- tual Structures: Applications, Implementation and Theory, vol. 954 of Lecture Notes in Artificial Intelligence, Springer Verlag, 1995.

[30] J. Leskovec, A. Rajaraman, J. Ullman, Mining of Massive Datasets, Cambridge University Press, 2010

[31] M. Najork, Detecting Spam web Pages through Content Analysis, International World Wide web Conference Comittee, Edinburgh, Scotland, 2006, pp. 83-92 [32] J.P. Norguet, B. Tshibasu-Kabeya, G. Bontempi, E. Zimanyi, A Page-

Classification Approach to Web Usage Semantic Analysis, Engineering Letters, 14:1, EL 14 1 21.

[33] L. Rao,WordPress Now Powers 22 Percent Of New Active websites In The U.S., August, 2011, TechCrunch

[34] C. Romero, P. G. Espejo, A. Zafra, J. R. Romero, S. Ventura, Web usage mi- ning for predicting final marks of students that use moodle courses, Computer Applications in Engineering Education, 21 (2013), pp. 135–146.

[35] C. Romero, S. Ventura, A. Zafra, P. D. Bra,Applying web usage mining for perso- nalizing hyperlinks in web-based adaptive educational systems, Computers & Edu- cation, 53 (2009), pp. 828–840.

[36] A. Singhal,Modern Information Retrieval: A Brief Overview, Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 2011, 24 (4): 35-43 [37] M. Spiliopoulou, L. C. Faulstich,Wum: a tool for web utilization analysis, in The

World Wide Web and Databases, Springer, 1999, pp. 184–203.

[38] N. Spirin, J. Han, Survey on web Spam Detection: Principles and Algorithms, ACM SIGKDD Explorations Newsletter, Volume 13, Issue 2, December 2011, pp.


[39] J. Srivastava, R. Cooley, M. Deshpande, T. Pang-Ning, Web usage mining: Di- scovery and applications of usage patterns from web data., SIGKDD Explorations, 1(2), 2000.

[40] R. Wille, Conceptual landscapes of knowledge: a pragmatic paradigm for knowle- dge processing, Proceedings of the International Symposium on Knowledge Repre- sentation, Use, and Storage Efficiency. Simon Fraser University, Vancouver 1997, 2-13.

[41] D.J. Watts,Nature 445, 489 (2007).

[42] Wille, R.: Conceptual Landscapes of Knowledge: a Pragmatic Paradigm for Knowledge Processing, In: Gaul, W.; Locarek-Junge H. (Eds.): Classification in the Information Age, Proceedings of the 22nd Annual Gfki Conference, Dresden, March 4-6, 1998, pp. 344–356.