As of August 2024, International Journal "Notes on Intuitionistic Fuzzy Sets" is being indexed in Scopus.
Please check our Instructions to Authors and send your manuscripts to nifs.journal@gmail.com. Next issue: March 2025.

Issue:Generalized net of the process of association rules discovery by Eclat algorithm using weather databases: Difference between revisions

From Ifigenia, the wiki for intuitionistic fuzzy sets and generalized nets
Jump to navigation Jump to search
New page: {{PAGENAME}} {{PAGENAME}} {{PAGENAME}} {{issue/title | title...
 
m 14
 
(One intermediate revision by one other user not shown)
Line 4: Line 4:
{{issue/title
{{issue/title
  | title          = Generalized net of the process of association rules discovery by Eclat algorithm using weather databases
  | title          = Generalized net of the process of association rules discovery by Eclat algorithm using weather databases
  | shortcut        = iwgn-13-1-10
  | shortcut        = iwgn-14-1-10
}}
}}
{{issue/author
{{issue/author
Line 21: Line 21:
}}
}}
{{issue/data
{{issue/data
  | conference      = 13<sup>th</sup> [[IWGN]], Burgas, 30 November 2013
  | conference      = 14<sup>th</sup> [[IWGN]], Burgas, 29-30 November 2013
  | issue          = [[International Workshop on Generalized Nets/13|Conference proceedings]], pages 1—10
  | issue          = [[International Workshop on Generalized Nets/14|Conference proceedings]], pages 1—10
  | file            = IWGN2013-14-01-10.pdf
  | file            = IWGN2013-14-01-10.pdf
  | format          = PDF
  | format          = PDF
  | size            = 158
  | size            = 158
  | abstract        =  
  | abstract        =  
  In the present paper, a Generated net mode
  In the present paper, a Generated net model is constructed to determine the possibility of forest fire by association rules. To model the process, we use frequent pattern mining by the Eclat algorithm. A pattern is considered to be frequent when it occurs in the data more often than a predefined minimum support frequency. Frequent pattern mining is a step of the process of association rules discovery. Eclat algorithm uses vertical data format for generating frequent patterns, with associative rules having the If A then B form. The proposed Generated net model should both fit well the input metrological observations, and correctly predict previously unknown weather parameters. It can be used for monitoring of the possibility of fire via frequent pattern mining depending on metrological conditions.  
l is constructed to determine the possibility
of forest fire by association rules. To model the process, we use frequent pattern mining by the
Eclat algorithm. A pattern is considered to be freq
uent when it occurs in the data more often than
a predefined minimum support frequency. Frequent pattern mining is a step of the process of
association rules discovery. Eclat algorithm uses
vertical data format for generating frequent
patterns, with associative rules having the
If A then B
form. The proposed Generated net model
should both fit well the input metrological observat
ions, and correctly predict previously unknown
weather parameters. It can be used for monitoring
of the possibility of fire via frequent pattern
mining depending on metrological conditions.  


  | keywords        =  Generalized Net,
  | keywords        =  Generalized Net, Association rules, Weather databases, Frequent pattern mining, Data Mining, Knowledge Discovery.
Association rules, Weather databases, Frequent pattern mining, Data Mining, Knowledge Discovery.
  | ams            = 68Q85, 62H30.
  | ams            = 68Q85, 62H30.
  | references      =  
  | references      =  
Line 55: Line 39:
# Bureva, V. Generalized model of the process of the creating the association rules using Apriori algorithm, Annual of “Informatics” Section Union of Scientists in Bulgaria, Vo. 5, 2012, 73–83 (in Bulgarian).  
# Bureva, V. Generalized model of the process of the creating the association rules using Apriori algorithm, Annual of “Informatics” Section Union of Scientists in Bulgaria, Vo. 5, 2012, 73–83 (in Bulgarian).  
# Bureva, V. Generalized model of the process of the creating the association rules using Frequent Pattern-Growth Method, Annual of “Informatics” Section Union of Scientists in Bulgaria, 2013 (in bulgarian, in press).  
# Bureva, V. Generalized model of the process of the creating the association rules using Frequent Pattern-Growth Method, Annual of “Informatics” Section Union of Scientists in Bulgaria, 2013 (in bulgarian, in press).  
# Ghosh, S., Nag, A., Biswas, D., Singh, J.P., Biswas, S., Sarkar, D., Sarkar, P.P., Weather Data Mining using Artificial Neural Network,
# Ghosh, S., Nag, A., Biswas, D., Singh, J.P., Biswas, S., Sarkar, D., Sarkar, P.P., Weather Data Mining using Artificial Neural Network, Recent Advances in Intelligent Computational Systems (RAICS), IEEE, 2011, 192–195.  
Recent Advances in Intelligent Computational Systems (RAICS), IEEE, 2011, 192–195.  
# TKaur, G., Meteorological Data Mining Techniques: A Survey, International Journal of Emerging Technology and Advanced Engineering, Volume 2, Issue 8, August 2012, 325–327. http://www.ijetae.com/files/Volume2Issue8/IJETAE_0812_56.pdf.
# TKaur, G., Meteorological Data Mining Techniques: A Survey, International Journal of Emerging Technology and Advanced Engineering, Volume 2, Issue 8, August 2012, 325–327. http://www.ijetae.com/files/Volume2Issue8/IJETAE_0812_56.pdf
# Larose, D., Discovering Knowledge In Data. An Introduction To Data Mining, John Wiley& Sons, 2005.
# Todorova, M. Verification of Procedural Programs via Building their Generalized Nets Models. Proceedings of the 41. Spring Conference of the Union of Bulgarian Mathematicians, Mathematics and Education in Mathematics, 2012, 259–265.
# Tan, P., M. Steinbach, V. Kumar, Introduction to Data Mining, Addison-Wesley, 2006.
# Trifonov, T., K. Georgiev, K. Atanassov. Software for modelling with Generalised Nets, Issues in Intuitionistic Fuzzy Sets and Generalized Nets, Vol. 6, 2008, 36–42.  
# Kalyankar, M., S. Alaspurkar, Data Mining Technique to Analyse the Metrological Data, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 2, February 2013, 114–118. http://www.ijarcsse.com
  | citations      =  
  | citations      =  
  | see-also        =  
  | see-also        =  
}}
}}

Latest revision as of 18:08, 4 February 2014

shortcut
http://ifigenia.org/wiki/issue:iwgn-14-1-10
Title of paper: Generalized net of the process of association rules discovery by Eclat algorithm using weather databases
Author(s):
Veselina Bureva
"Prof. Asen Zlatarov" University, 1 “Prof. Yakimov” Blvd, Burgas–8010, Bulgaria
vesito_ka@abv.bg
Evdokia Sotirova
Prof. Asen Zlatarov” University, 1 “Prof. Yakimov” Blvd, Burgas–8010, Bulgaria
esotirova@btu.bg
Presented at: 14th IWGN, Burgas, 29-30 November 2013
Published in: Conference proceedings, pages 1—10
Download:  PDF (158  Kb, File info)
Abstract: In the present paper, a Generated net model is constructed to determine the possibility of forest fire by association rules. To model the process, we use frequent pattern mining by the Eclat algorithm. A pattern is considered to be frequent when it occurs in the data more often than a predefined minimum support frequency. Frequent pattern mining is a step of the process of association rules discovery. Eclat algorithm uses vertical data format for generating frequent patterns, with associative rules having the If A then B form. The proposed Generated net model should both fit well the input metrological observations, and correctly predict previously unknown weather parameters. It can be used for monitoring of the possibility of fire via frequent pattern mining depending on metrological conditions.
Keywords: Generalized Net, Association rules, Weather databases, Frequent pattern mining, Data Mining, Knowledge Discovery.
AMS Classification: 68Q85, 62H30.
References:
  1. Agrawal, R., Imielinski T., And Swami A., Mining Association Rules Between Sets Of Items In Large Databases, in Proceedings of ACM-SIGMOD Conference, Washington, DC, 1993
  2. Atanassov, K. Generalized Nets. World Scientific, Singapore, 1991.
  3. Atanassov, K. On Generalized Nets Theory. Prof. M. Drinov Academic Publishing House, Sofia, 2007.
  4. Bureva, V. Methods for extracting patterns from databases, Management and Education, University "Prof. Asen Zlatarov", Burgas, Vol. 8 (4), 2012, 255–258 (in Bulgarian).
  5. Bureva, V. Algorithms for associative rule mining, Management and Education, University "Prof. Asen Zlatarov", Burgas, Vol. 9 (6) 2013, 121–128 (in Bulgarian).
  6. Bureva, V. Generalized model of the process of the creating the association rules using Apriori algorithm, Annual of “Informatics” Section Union of Scientists in Bulgaria, Vo. 5, 2012, 73–83 (in Bulgarian).
  7. Bureva, V. Generalized model of the process of the creating the association rules using Frequent Pattern-Growth Method, Annual of “Informatics” Section Union of Scientists in Bulgaria, 2013 (in bulgarian, in press).
  8. Ghosh, S., Nag, A., Biswas, D., Singh, J.P., Biswas, S., Sarkar, D., Sarkar, P.P., Weather Data Mining using Artificial Neural Network, Recent Advances in Intelligent Computational Systems (RAICS), IEEE, 2011, 192–195.
  9. TKaur, G., Meteorological Data Mining Techniques: A Survey, International Journal of Emerging Technology and Advanced Engineering, Volume 2, Issue 8, August 2012, 325–327. http://www.ijetae.com/files/Volume2Issue8/IJETAE_0812_56.pdf.
  10. Larose, D., Discovering Knowledge In Data. An Introduction To Data Mining, John Wiley& Sons, 2005.
  11. Tan, P., M. Steinbach, V. Kumar, Introduction to Data Mining, Addison-Wesley, 2006.
  12. Kalyankar, M., S. Alaspurkar, Data Mining Technique to Analyse the Metrological Data, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 2, February 2013, 114–118. http://www.ijarcsse.com
Citations:

The list of publications, citing this article may be empty or incomplete. If you can provide relevant data, please, write on the talk page.