An improved apriori algorithm based on support weight matrix for data mining in transaction database

Data mining is a process to discover hidden information or knowledge automatically from huge database. In order to reduce the number of scanning databases and reflect the importance of different items and transaction so as to extract more valuable information, an improved Apriori algorithm is proposed in this paper, which is to build the 0–1 transaction matrix by scanning transaction database for getting the weighted support and confidence. The items and transactions is weighted to reflect the importance in the transaction database. The experiment results, both qualitative and quantitative, have shown that our improved algorithm shortens the running time and reduces the memory requirement and the number of I/O operations. Meanwhile, the support for rare items tends to increase, while the support for other items decreases slightly, thus the hidden and valuable items can be effectively extracted.

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