Aiming at the redundancy problem of association rules in the mining process of mining association rules, a kind of shortest antecedent set algorithm (SASA) for mining association rules is proposed in this paper, and the algorithm is supported by the set-enumeration tree structure. The proposed algorithm is able to record a kind of subset for association rules without any information loss, and t...

Detailed elaborations are presented for the idea on two-step frequent itemsets Apriori algorithm of Association Rules. An improved method called Improved Apriori algorithm is brought forward owing to the disadvantages of Apriori algorithm. Moreover, based on Improved Apriori algorithm, data mining for breast-cancers is carried out for the relationship between breast-cancer recurrences and other...

Complex networks and networked data mining p. 10 In-depth data mining and its application in stock market p. 13 Relevance of counting in data mining tasks p. 14 Term graph model for text classification p. 19 A latent usage approach for clustering Web transaction and building user profile p. 31 Mining quantitative association rules on overlapped intervals p. 43 An approach to mining local causal...

The mining association algorithm is one of the most popular data mining algorithms to derive association rules at high speed from huge databases. We have been developing navigation systems for semi-structured data like as Web data and bibliographic data. To navigate beginners, our systems give the association rules derived by the algorithm. However; the algorithm tends to derive those rules tha...

Association Rule mining is very efficient technique for finding strong relation between correlated data. The correlation of data gives meaning full extraction process. For the discovering frequent items and the mining of positive rules, a variety of algorithms are used such as Apriori algorithm and tree based algorithm. But these algorithms do not consider negation occurrence of the attribute i...

Association rule mining has attracted wide attention in both research and application area recently. Mining multilevel association rules is one of the most important branch of it. This paper introduces an improved apriori algorithm so called FP-growth algorithm that will help resolve two neck-bottle problems of traditional apriori algorithm and has more efficiency than original one. New FP tree...

It is an important part of research content in data mining to discover association rules from large scale database, the main problem of which is frequent itemsets mining. The classical Apriori algorithm is an efficient one for that. Aimed at the performance bottlenecks of multiply scanning the database and generating a large quantity of candidate itemsets in Apriori algorithm, an improved algor...

Clustering is one of the important data mining techniques. k-Means [1] is one of the most important algorithm for Clustering. Traditional k-Means algorithm selects initial centroids randomly and in k-Means algorithm result of clustering highly depends on selection of initial centroids. k-Means algorithm is sensitive to initial centroids so proper selection of initial centroids is necessary. Thi...

Many methods have been proposed to mine fuzzy association rules from databases with crisp values in order to help decision-makers make good decisions and tackle new types of problems. However, most real-world problems present a certain degree of imprecision. Various studies have been proposed to mine fuzzy association rules from imprecise data but they assume that the membership functions are k...