Finding Fuzzy Association Rules Using FWFP-Growth with Linguistic Supports and Confidences
In data mining, the association rules are used to search
for the relations of items of the transactions database. Following the
data is collected and stored, it can find rules of value through
association rules, and assist manager to proceed marketing strategy
and plan market framework. In this paper, we attempt fuzzy partition
methods and decide membership function of quantitative values of
each transaction item. Also, by managers we can reflect the
importance of items as linguistic terms, which are transformed as
fuzzy sets of weights. Next, fuzzy weighted frequent pattern growth
(FWFP-Growth) is used to complete the process of data mining. The
method above is expected to improve Apriori algorithm for its better
efficiency of the whole association rules. An example is given to
clearly illustrate the proposed approach.
[1] Agrawal, R., Mannila, H., Srikant, R., Toivonen, H. & Verkamo, A. I.
"Fast discovery of association rules", in U. M. Fayyad, G.
Piatesky-Shapiro, P., Smyth, and R. Uthursamy, Advances in Knowledge
Discovery and Data Mining , AAAI Press, Menlo Park, pp.307-328,
1996.
[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: Concepts and Techniques. Morgan
Kaufmann, San Francisco, 2001.
[5] Han, J., Pei, J., and Y., "Mining frequent patterns without candidate
generation," Proceedings of the ACM SIGMOD International Conference
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] Hong, Tp., Wang, T. T., Wang, S. L., & Chien, B. C., "Learning a
coverage set of maximally general fuzzy rules by rough sets," Expert
Systems with Applications, vol.19, no.2, pp.97-103, 2000.
[8] Hu, Y. C., Chen, R. S. & Tzeng, G. H., "Finding fuzzy classification rules
using data mining techniques ," Pattern Recognition Letters, vol.24,
pp.509-519, 2003.
[9] Hu. Y. C., "Mining association rules at a concept hierarchy using fuzzy
partition," Journal of Information Management, vol.13, no.3, pp.63-80,
July, 2006.
[10] T.P. Hong, M. J. Chiang & S. L. Wang, "Fuzzy weighted data mining
from quantitative transactions with linguistic minimum supports and
confidences," International Journal of Fuzzy Systems, vol8, no4, pp.
173-182, Dec. 2006.
[11] Ishibuchi, H., Nakashima, T., & Murata, T., "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.
[12] Ishibuchi, H., Nozaki, K., Yamamoto, N. and Tanaka, H., "Selecting
fuzzy if-then rules for classification problems using genetic algorithms,"
IEEE transactions on fuzzy systems, vol.3, no.3, pp.260-270, 1995.
[13] 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.
[14] Myra, S., "Web useage mining for web site evaluation", Communications
of the ACM, vol.43, no.8, pp.127-134, 2000.
[15] Pedrycz, W. "Why triangular membership functions?," Fuzzy Sets and
Systems, vol.64, pp.21-30, 1994.
[16] Pedrycz, W., Gomide, F., An introduction of fuzzy sets: analysis and
design. MIT Press, Cambridge, 1998.
[17] Wang, L. X. and Mendel, J. M. "Generating fuzzy rules by learning from
examples," IEEE Transactions on Systems, Man, and Cybernetics, vol. 22,
no. 6,pp. 1414-1427, 1992.
[18] Yuan Y. and Shaw., M. J. "Induction of fuzzy decision trees," Fuzzy Sets
and Systems , vol.69 , pp.125-139, 1995.
[19] Zadeh, L. A. "The concept of linguistic variable and its application to
approximate reasoning," Information Science (Part 1), vol. 8, no. 3,
pp.199-249, 1975a.
[20] Zadeh, L. A. "The concept of linguistic variable and its application to
approximate reasoning," Information Science (Part 2), vol. 8, no. 4,
pp.301-357, 1975b.
[21] Zadeh, L. A. "The concept of linguistic variable and its application to
approximate reasoning," Information Science (Part 3), vol. 9, no.1,
pp:43-80, 1976.
[22] Zimmermann, H.-J., Fuzzy sets, Decision making and expert systems.
Kluwer, Boston, 1991.
[1] Agrawal, R., Mannila, H., Srikant, R., Toivonen, H. & Verkamo, A. I.
"Fast discovery of association rules", in U. M. Fayyad, G.
Piatesky-Shapiro, P., Smyth, and R. Uthursamy, Advances in Knowledge
Discovery and Data Mining , AAAI Press, Menlo Park, pp.307-328,
1996.
[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: Concepts and Techniques. Morgan
Kaufmann, San Francisco, 2001.
[5] Han, J., Pei, J., and Y., "Mining frequent patterns without candidate
generation," Proceedings of the ACM SIGMOD International Conference
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] Hong, Tp., Wang, T. T., Wang, S. L., & Chien, B. C., "Learning a
coverage set of maximally general fuzzy rules by rough sets," Expert
Systems with Applications, vol.19, no.2, pp.97-103, 2000.
[8] Hu, Y. C., Chen, R. S. & Tzeng, G. H., "Finding fuzzy classification rules
using data mining techniques ," Pattern Recognition Letters, vol.24,
pp.509-519, 2003.
[9] Hu. Y. C., "Mining association rules at a concept hierarchy using fuzzy
partition," Journal of Information Management, vol.13, no.3, pp.63-80,
July, 2006.
[10] T.P. Hong, M. J. Chiang & S. L. Wang, "Fuzzy weighted data mining
from quantitative transactions with linguistic minimum supports and
confidences," International Journal of Fuzzy Systems, vol8, no4, pp.
173-182, Dec. 2006.
[11] Ishibuchi, H., Nakashima, T., & Murata, T., "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.
[12] Ishibuchi, H., Nozaki, K., Yamamoto, N. and Tanaka, H., "Selecting
fuzzy if-then rules for classification problems using genetic algorithms,"
IEEE transactions on fuzzy systems, vol.3, no.3, pp.260-270, 1995.
[13] 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.
[14] Myra, S., "Web useage mining for web site evaluation", Communications
of the ACM, vol.43, no.8, pp.127-134, 2000.
[15] Pedrycz, W. "Why triangular membership functions?," Fuzzy Sets and
Systems, vol.64, pp.21-30, 1994.
[16] Pedrycz, W., Gomide, F., An introduction of fuzzy sets: analysis and
design. MIT Press, Cambridge, 1998.
[17] Wang, L. X. and Mendel, J. M. "Generating fuzzy rules by learning from
examples," IEEE Transactions on Systems, Man, and Cybernetics, vol. 22,
no. 6,pp. 1414-1427, 1992.
[18] Yuan Y. and Shaw., M. J. "Induction of fuzzy decision trees," Fuzzy Sets
and Systems , vol.69 , pp.125-139, 1995.
[19] Zadeh, L. A. "The concept of linguistic variable and its application to
approximate reasoning," Information Science (Part 1), vol. 8, no. 3,
pp.199-249, 1975a.
[20] Zadeh, L. A. "The concept of linguistic variable and its application to
approximate reasoning," Information Science (Part 2), vol. 8, no. 4,
pp.301-357, 1975b.
[21] Zadeh, L. A. "The concept of linguistic variable and its application to
approximate reasoning," Information Science (Part 3), vol. 9, no.1,
pp:43-80, 1976.
[22] Zimmermann, H.-J., Fuzzy sets, Decision making and expert systems.
Kluwer, Boston, 1991.
@article{"International Journal of Information, Control and Computer Sciences:64450", author = "Chien-Hua Wang and Chin-Tzong Pang", title = "Finding Fuzzy Association Rules Using FWFP-Growth with Linguistic Supports and Confidences", abstract = "In data mining, the association rules are used to search
for the relations of items of the transactions database. Following the
data is collected and stored, it can find rules of value through
association rules, and assist manager to proceed marketing strategy
and plan market framework. In this paper, we attempt fuzzy partition
methods and decide membership function of quantitative values of
each transaction item. Also, by managers we can reflect the
importance of items as linguistic terms, which are transformed as
fuzzy sets of weights. Next, fuzzy weighted frequent pattern growth
(FWFP-Growth) is used to complete the process of data mining. The
method above is expected to improve Apriori algorithm for its better
efficiency of the whole association rules. An example is given to
clearly illustrate the proposed approach.", keywords = "Association Rule, Fuzzy Partition Methods,
FWFP-Growth, Apiroir algorithm", volume = "3", number = "5", pages = "1467-9", }
