Abstract
Aiming at the shortcomings of the traditional "support-confidence" association rules mining framework and the problems of mining negative association rules, the concept of interestingness measure is introduced. Analyzed the advantages and disadvantages of some commonly used interestingness measures at present, and combined the cosine measure on the basis of the interestingness measure model based on the difference idea, and proposed a new interestingness measure model. The interestingness measure can effectively express the relationship between the antecedent and the subsequent part of the rule. According to this model, an association rules mining algorithm based on the interestingness measure fusion model is proposed to improve the accuracy of mining. Experiments show that the algorithm has better performance and can effectively help mining positive and negative association rules.
Highlights
Association rules mining is an important research direction in the field of data mining, and it has been widely used in financial, meteorological, medical and other fields[1]
Since the objective interestingness measure does not consider the subjective influence of users and can better reflect the actual situation, most of the research on the interestingness measure model is carried out on the objective interestingness measure[10].Based on this, this paper proposes an objective interestingness measure, which is based on the difference idea interestingness measure[3], combined with the cosine measure, so that the interestingness measure can effectively express the relationship between the antecedent and the subsequent part of the rule, and the interestingness measure is used to solve the problem of mining positive and negative association rules
By combining the cosine measure, the interestingness measure model based on the difference idea is improved, and the new interestingness measure model is obtained as follows: IIIIIIll
Summary
Association rules mining is an important research direction in the field of data mining, and it has been widely used in financial, meteorological, medical and other fields[1]. According to the mining problems of positive and negative association rules[3] and the shortcomings of traditional mining frameworks, some researchers have proposed the concept of interestingness measure[4]. Since the objective interestingness measure does not consider the subjective influence of users and can better reflect the actual situation, most of the research on the interestingness measure model is carried out on the objective interestingness measure[10].Based on this, this paper proposes an objective interestingness measure, which is based on the difference idea interestingness measure[3], combined with the cosine measure, so that the interestingness measure can effectively express the relationship between the antecedent and the subsequent part of the rule, and the interestingness measure is used to solve the problem of mining positive and negative association rules
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