@article{(Open Science Index):https://publications.waset.org/pdf/11140,
	  title     = {Applying Fuzzy FP-Growth to Mine Fuzzy Association Rules},
	  author    = {Chien-Hua Wang and  Wei-Hsuan Lee and  Chin-Tzong Pang},
	  country	= {},
	  institution	= {},
	  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.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {4},
	  number    = {5},
	  year      = {2010},
	  pages     = {986 - 992},
	  ee        = {https://publications.waset.org/pdf/11140},
	  url   	= {https://publications.waset.org/vol/41},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 41, 2010},