http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Eloy Gonzales (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
-
Class Association Rule Mining with Correlation Measuresusing Genetic Network Programming
Eloy Gonzales,Shingo Mabu,Karla Taboada,Kaoru Shimada,Kotaro Hirasawa 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
Association rule mining is one of the tasks of datamining and it has been extensively studied recently. As a consequence, several methods for extractin gas sociation rules have been developed during the last years. Most of the muse the supportand confidence framework to extract the association rules. Researches are able to extract strong rules using this framework. However these measures are not good enough to solve the quality problems of the rules. A new data mining method using Genetic Network Programming(GNP) has also been developed recently which uses the χ<SUP>2</SUP> (chi-squared) as a correlation measure and its effectiveness has been shown for different data sets[1][2]. To enhance the correlation degree and comprehensibility of association rule, several correlation measures including lift, χ<SUP>2</SUP>, all-confidence and cosineare studied in this paper when they are in corporated in the conventional GNP based mining algorithm. A comparison between the correlation measures is made in the simulations when they are incorporated separately in to the GNP based mining method. Finally, the association rules extracted using different correlation measures are applied to the classification problems and the prediction accuracies of them are evaluated.
-
Karla Taboada,Shingo Mabu,Eloy Gonzales,Kaoru Shimada,Kotaro Hirasawa 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
One of the most important issues in any association rule mining is the interpretation and evaluation of discov-eredrules. Thus, most algorithms employ the support-confidence framework for evaluating association and classification rules. Unfortunately, recent studies show that the supportand confidence measures are insufficient for filtering outun in-teresting association rules, for instance, even strong association rules can be uninteresting and misleading. To deal with this limitation, the support-confidence framework can be suplemented with additional interestingness measures based on statistical significance and correlation analysis. In this paper, a novel fuzzy association rule-based classification approach is proposed, where χ<SUP>2</SUP> is applied as a correlation measure. The algorithm is based on Genetic Network Programming(GNP) and discover comprehensible fuzzy association rules potentially useful for classification. GNP is an evolutionary optimization algorithm that uses directed graph structures as genes instead of strings and trees of Genetic Algorithms(GA) and Genetic Programming(GP), respectively. This feature contributes to creating quite compact programs and im-plicitly memorizing pastaction sequences. The proposed model consists of two major phases: 1) generating fuzzy class association rules by using GNP, 2) building a classifier based on the extracted fuzzy rules. In the first phase, χ<SUP>2</SUP> is used for computing the correlation of the rules to be integrated into the classifier. In the second phase, the χ<SUP>2</SUP> value is used as a weight of the rule when calculating the matching degree of the rule with new data. The performance of the proposed algorithm has been compared with other relevant algorithms and the experimental results have shown the advantages an effectiveness of the proposed model.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
