Efficient Approach for large Database Compressed in Association Mining

computer science & engineering

In an era of knowledge explosion, the growth of data increases rapidly day by day. Since data storage is a limited resource, how to reduce the data space in the process becomes a challenge issue. Data compression provides a good solution which can lower the required space. Data mining has many useful applications in recent years because it can help users discover interesting knowledge in large databases. However, existing compression algorithms are not appropriate for data mining. In this research a new approach called Mining Merged Transactions with the Quantification Table was proposed to solve these problems. Mining Merged Transactions with the Quantification Table uses the relationship of transactions to merge related transactions and builds a quantification table to prune the candidate item sets which are impossible to become frequent in order to improve the performance of mining association rules. The experiments show that Mining Merged Transactions with the Quantification Table perform better then existing approaches.

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