Applying Fuzzy FP-Growth to Mine Fuzzy Association Rules
In data mining, the association rules are used to find
for the associations between the different items of the transactions
database. As the data collected and stored, rules of value can be found
through association rules, which can be applied to help managers
execute marketing strategies and establish sound market frameworks.
This paper aims to use Fuzzy Frequent Pattern growth (FFP-growth)
to derive from fuzzy association rules. At first, we apply fuzzy
partition methods and decide a membership function of quantitative
value for each transaction item. Next, we implement FFP-growth
to deal with the process of data mining. In addition, in order to
understand the impact of Apriori algorithm and FFP-growth algorithm
on the execution time and the number of generated association
rules, the experiment will be performed by using different sizes of
databases and thresholds. Lastly, the experiment results show FFPgrowth
algorithm is more efficient than other existing methods.
[1] Agrawal, R. and Srikant, R., "Fast algorithms for mining association
rules," in Proceedings of 1994 International Conference on Very Large
Data Bases, pp.487-499, 1994.
[2] Berry, M. and Linoff, G., Data Mining Techniques: for marketing, sales,
and customer support, John Wiley & Sons, NY, 1997.
[3] Chen, S. M., Jong, W. T., "Fuzzy query translation for relational database
systems," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 27,
no. 4, pp. 714-721, 1997.
[4] Han, J. W. and Kamber, M., Data Mining : Concepts and Techniques,
Morgan Kaufmann, San Francisco, 2001.
[5] Han, J., Pei, J., and Yin, Y., "Mining Frequent Patterns without Candidate
Generation," in Proc. ACM SIGMOD Int. Conf. on Management of Data,
pp, 1-12, 2000.
[6] Hong, T. P., & Chen, J. B., "Find relevant attributes and membership
functions," Fuzzy Sets and Systems, Vol. 103, no. 3, pp.389-404, 1999.
[7] Hu Y. C,. "Mining association rules at a concept hierarchy using fuzzy
partition," Journal of Information Management, Vol. 13, no.3, pp.63-80,
2006.
[8] Hu, Y. C., Chen, R. S. and Tzeng, G. H., "Finding Fuzzy Classification
Rules Using Data Mining Techniques," Pattern Recognition Letters, vol.
24, pp.509-519, 2003.
[9] Ishibuchi, H., Nakashima, T., and Murata, T. (1999), "Performance
Evaluation of Fuzzy Classifier Systems for Multidimensional Pattern
Classification Problems," IEEE Transactions on Systems, Man, and Cybernetics,
Vol. 29, no.5, pp.601-618, 1999.
[10] Jang, J. S. R., Sun, C. T. and Mizutani, E., Neuro-fuzzy and soft computing:
a computational approach to learning and machine intelligence,
Prentic-Hall, NJ, 1997.
[11] Myra, S., "Web usage mining for web site evaluation," Communications
of the ACM, Vol. 43, pp. 21-30, 1994.
[12] Pedrycz, W., "Why triangular membership functions?," Fuzzy Sets and
Systems, Vol. 64, pp. 21-30, 1994.
[13] Tan. P. N., Michael Mteinbach, Vipin Kumar, Introduction to Data
Mining, NY: Addison Wesley, 2005
[14] Wang, L. X. and Mendel, J. M. (1992), "Generating Fuzzy Rules by
Learning from Examples", IEEE Transactions on Systems, Man, and
Cybernetisc, Vol. 22, no.6, pp.1414-1427, 1992.
[15] Zadeh, L. A. (1965), "Fuzzy Sets," Information Control, vol. 8, no.3,
pp.338-353, 1965.
[16] Zadeh, L. A. (1975), "The Concept of a Linguistic Variable and Its
Application to Approximate Reasoning," Information Science (part 1),
Vol. 8, no. 3, pp.199-249, 1975.
[17] Zadeh, L. A. (1975), "The Concept of a Linguistic Variable and Its
Application to Approximate Reasoning," Information Science (part 2),
Vol. 8, no. 4, pp.301-357, 1975.
[18] Zadeh, L. A. (1976), "The Concept of a Linguistic Variable and Its
Application to Approximate Reasoning," Information Science (part 3),
Vol. 9, no. 1, pp.43-80, 1976.
[19] Zimmermann, H. -J., Fuzzy sets, Decision making and expert systems,
Kluwer, Boston, 1991.
[1] Agrawal, R. and Srikant, R., "Fast algorithms for mining association
rules," in Proceedings of 1994 International Conference on Very Large
Data Bases, pp.487-499, 1994.
[2] Berry, M. and Linoff, G., Data Mining Techniques: for marketing, sales,
and customer support, John Wiley & Sons, NY, 1997.
[3] Chen, S. M., Jong, W. T., "Fuzzy query translation for relational database
systems," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 27,
no. 4, pp. 714-721, 1997.
[4] Han, J. W. and Kamber, M., Data Mining : Concepts and Techniques,
Morgan Kaufmann, San Francisco, 2001.
[5] Han, J., Pei, J., and Yin, Y., "Mining Frequent Patterns without Candidate
Generation," in Proc. ACM SIGMOD Int. Conf. on Management of Data,
pp, 1-12, 2000.
[6] Hong, T. P., & Chen, J. B., "Find relevant attributes and membership
functions," Fuzzy Sets and Systems, Vol. 103, no. 3, pp.389-404, 1999.
[7] Hu Y. C,. "Mining association rules at a concept hierarchy using fuzzy
partition," Journal of Information Management, Vol. 13, no.3, pp.63-80,
2006.
[8] Hu, Y. C., Chen, R. S. and Tzeng, G. H., "Finding Fuzzy Classification
Rules Using Data Mining Techniques," Pattern Recognition Letters, vol.
24, pp.509-519, 2003.
[9] Ishibuchi, H., Nakashima, T., and Murata, T. (1999), "Performance
Evaluation of Fuzzy Classifier Systems for Multidimensional Pattern
Classification Problems," IEEE Transactions on Systems, Man, and Cybernetics,
Vol. 29, no.5, pp.601-618, 1999.
[10] Jang, J. S. R., Sun, C. T. and Mizutani, E., Neuro-fuzzy and soft computing:
a computational approach to learning and machine intelligence,
Prentic-Hall, NJ, 1997.
[11] Myra, S., "Web usage mining for web site evaluation," Communications
of the ACM, Vol. 43, pp. 21-30, 1994.
[12] Pedrycz, W., "Why triangular membership functions?," Fuzzy Sets and
Systems, Vol. 64, pp. 21-30, 1994.
[13] Tan. P. N., Michael Mteinbach, Vipin Kumar, Introduction to Data
Mining, NY: Addison Wesley, 2005
[14] Wang, L. X. and Mendel, J. M. (1992), "Generating Fuzzy Rules by
Learning from Examples", IEEE Transactions on Systems, Man, and
Cybernetisc, Vol. 22, no.6, pp.1414-1427, 1992.
[15] Zadeh, L. A. (1965), "Fuzzy Sets," Information Control, vol. 8, no.3,
pp.338-353, 1965.
[16] Zadeh, L. A. (1975), "The Concept of a Linguistic Variable and Its
Application to Approximate Reasoning," Information Science (part 1),
Vol. 8, no. 3, pp.199-249, 1975.
[17] Zadeh, L. A. (1975), "The Concept of a Linguistic Variable and Its
Application to Approximate Reasoning," Information Science (part 2),
Vol. 8, no. 4, pp.301-357, 1975.
[18] Zadeh, L. A. (1976), "The Concept of a Linguistic Variable and Its
Application to Approximate Reasoning," Information Science (part 3),
Vol. 9, no. 1, pp.43-80, 1976.
[19] Zimmermann, H. -J., Fuzzy sets, Decision making and expert systems,
Kluwer, Boston, 1991.
@article{"International Journal of Information, Control and Computer Sciences:60188", author = "Chien-Hua Wang and Wei-Hsuan Lee and Chin-Tzong Pang", title = "Applying Fuzzy FP-Growth to Mine Fuzzy Association Rules", abstract = "In data mining, the association rules are used to find
for the associations between the different items of the transactions
database. As the data collected and stored, rules of value can be found
through association rules, which can be applied to help managers
execute marketing strategies and establish sound market frameworks.
This paper aims to use Fuzzy Frequent Pattern growth (FFP-growth)
to derive from fuzzy association rules. At first, we apply fuzzy
partition methods and decide a membership function of quantitative
value for each transaction item. Next, we implement FFP-growth
to deal with the process of data mining. In addition, in order to
understand the impact of Apriori algorithm and FFP-growth algorithm
on the execution time and the number of generated association
rules, the experiment will be performed by using different sizes of
databases and thresholds. Lastly, the experiment results show FFPgrowth
algorithm is more efficient than other existing methods.", keywords = "Data mining, association rule, fuzzy frequent patterngrowth.", volume = "4", number = "5", pages = "970-7", }