چکیده مقاله
Associative classifier algorithms combine two data mining paradigms, namely sample classification andassociation rule mining These methods are very interesting for building an accurate classification model in a wide area of real world applications Lately, many methods have been presented to integrate associative classifiers with fuzzy set theory, in order to improve the quality of previous algorithms This paper presents a three step fuzzy frequent pattern FFP based classifier which uses an Apriori like algorithm to generate a large number of FFPs from each data class Our algorithm in the second stepselects a subset of useful FFPs and removes redundant ones Finally, in order to tune the boundaries between various data classes, we use a confidence improvement process We tested our algorithm on six real world datasets and compared the achieved results with two well known fuzzy associative classifier algorithms
کلیدواژهها
نویسندگان
Alireza Hekmatinia
Faculty of Electrical and Computer Engineering Tarbiat Modares University, Tehran, Iran
Mohammad Saniee Abadeh
Faculty of Electrical and Computer Engineering Tarbiat Modares University, Tehran, Iran
شیوه ارجاع
Hekmatinia, Alireza and Saniee Abadeh, Mohammad,1394,An Accurate Fuzzy Frequent Pattern Based Classifier Using Confidence Tuning,1st National Conference on Soft Computing,Rasht
ارائهشده در
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