The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON COMPUTERS
Transactions ID Number: 29-605
Full Name: Wei-qing Sun
Position: Ph.D. Candidate
Age: ON
Sex: Male
Address: Class B0703192, 800 Dongchuan Road, Shanghai
Country: CHINA
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E-mail address: neilswq@gmail.com
Other E-mails: sidswq@hotmail.com
Title of the Paper: TRANSACTION-ITEM ASSOCIATION MATRIX-BASED FREQUENT PATTERN NETWORK MINING ALGORITHM IN LARGE-SCALE TRANSACTION DATABASE
Authors as they appear in the Paper: Wei-qing Sun, Cheng-min Wang, Tie-yan Zhang, Yan Zhang
Email addresses of all the authors: neilswq@gmail.com,wangchengmin@sjtu.edu.cn,tieyanzhang@163.com,zhang_yan@sjtu.edu.cn
Number of paper pages: 10
Abstract: Abstract: - To increase the efficiency of data mining is the emphasis in this field at present. Through the establishment of transaction-item association matrix, this paper changes the process of association rule mining to elementary matrix operation, which makes the process of data mining clear and simple. Compared with algorithms like Apriori, this method avoids the demerit of traversing the database repetitiously, and increases the efficiency of association rule mining obviously in the use of sparse storage technique for large-scale matrix. To incremental type of transaction matrix, it can also make the maintainment of association rule more convenient in the use of partitioning calculation technique of matrix. On the other and, aiming at the demerits in FP-growth algorithm, this paper proposes a FP-network model which compresses the data needed in association rule mining in a FP-network. Compared with the primary FP-tree model, the FP-network proposed is undirec!
ted, which enlarge the scale of transaction storage; furthermore, the FP-network is stored through the definition of transaction-item association matrix, it is convenient to make association rule mining on the basic of defining node capability. Experiment results show that the FP-network mining association rule algorithm proposed by this paper not only inherits the merits of FP-growth algorithm, but also maintains and updates data conveniently. It improves the efficiency of association rule mining significantly.
Keywords: Association rule, Association matrix, Data mining, FP-growth algorithm, FP-network algorithm, Frequent itemset
EXTENSION of the file: .doc
Special (Invited) Session: FREQUENT PATTERN NETWORK MINING ALGORITHM BASED ON TRANSACTION-ITEM ASSOCIATION MATRIX
Organizer of the Session: 620-519
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