An Incremental High-Utility Mining Algorithm with Transaction Insertion

Association-rule mining is commonly used to discover useful and meaningful patterns from a very large database. It only considers the occurrence frequencies of items to reveal the relationships among itemsets. Traditional association-rule mining is, however, not suitable in real-world applications since the purchased items from a customer may have various factors, such as profit or quantity. High-utility mining was designed to solve the limitations of association-rule mining by considering both the quantity and profit measures. Most algorithms of high-utility mining are designed to handle the static database. Fewer researches handle the dynamic high-utility mining with transaction insertion, thus requiring the computations of database rescan and combination explosion of pattern-growth mechanism. In this paper, an efficient incremental algorithm with transaction insertion is designed to reduce computations without candidate generation based on the utility-list structures. The enumeration tree and the relationships between 2-itemsets are also adopted in the proposed algorithm to speed up the computations. Several experiments are conducted to show the performance of the proposed algorithm in terms of runtime, memory consumption, and number of generated patterns.

Mining Maximal Frequent Patterns over Data Stream Based on Time Decaying

A new algorithm DSMFP-Miner was proposed. When the data stream reach continuously, a maximal frequent pattern tree called MFP-Tree is adopted to maintain the transactions in data screams dynamically. Transactions in the same Transaction-sensitive sliding window are set to own the same “importance”. Besides, the support of the transactions in old window is decayed to reduce their influence to mining results, and infrequent patterns and overdue patterns are deleted periodically. In the mining process, the algorithm put an enumeration tree with each node of MFP-Tree as root as the search space, and use the "depth-first" search strategy to mining the maximal frequent patterns with this node as a suffix.