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High-utility item sets mining (HUIM) is a critical issue to concern the quality and profit factors to mine high utility item sets (HUIs). Many algorithms have been proposed to efficiently mine HUIs from a static database. The discovered HUIs may become invalid or new HUIs may arise when transactions are modified in the original database. In the past, prelarge concept was proposed to efficiently maintain...
In this paper, we present an incremental mining algorithm for handling the mining problem from inserted transactions. The algorithm is based on the Dynamic Bit-Vector (DBV) structure and pre-large item sets. The DBV structure facilitates the processes for maintaining large and pre-large item sets. The pre-large concept is used to reduce the number of database scans. The experimental results show that...
The fast updated frequent pattern tree (FUFP-tree) is an efficient data structure for association-rule mining. Hong et al. (2009) proposed an approach for the maintenance of the FUFP-tree structure after the deletion of transactions. However, all transactions in the original database might need to be rescanned to determine the occurrence of infrequent items, which were not stored during the mining...
In the past, pre-large fast-updated sequential pattern trees (pre-large FUSP tree) were proposed for efficiently mining large sequences for record insertion and deletion, respectively. In this paper, we thus proposed a maintenance approach for efficiently maintaining pre-large FUSP trees and effectively deriving desired large sequences when data in databases are modified. Experimental results also...
We have previously proposed the high utility pattern (HUP) tree for utility mining. In this paper, we further handle the problem of maintaining the HUP tree in dynamic databases. A HUP maintenance algorithm has thus been proposed for efficiently handling new transactions. The proposed algorithm can reduce the cost of re-constructing the HUP tree when new transactions are inserted. Experimental results...
In the past, the fast-updated sequential-pattern tree (call FUSP-tree) structure was proposed for mining sequential patterns from a set of customer sequences. An incremental mining algorithm was also designed for handling newly added transactions. Since data may also be deleted in real applications, an FUSP-tree maintenance algorithm for deletion of customer sequences is thus proposed in this paper...
There are several algorithms proposed for maintaining the sequential patterns as records are inserted. In addition to record insertion, the pattern maintenance for record modification is also very important in the real-applications. In the past, we have proposed the fast updated sequential pattern tree (called FUSP tree) structure for handling record insertion. In this paper, we attempt to handle...
In this paper, we attempt to handle the maintenance of sequential patterns. New transactions may come from both the new customers and old customers. A fast updated sequential pattern tree (called FUSP-tree) structure is proposed to make the tree update process become easy. An incremental FUSP-tree maintenance algorithm is also proposed for reducing the execution time in reconstructing the tree. The...
In the past, the FUFP-tree maintenance algorithm is proposed to efficiently handle the association rules in incremental mining. In this paper, we attempt to modify the FUFP-tree maintenance algorithm for maintaining sequential patterns based on the concept of pre-large sequences to reduce the need for rescanning original databases in incremental mining. A fast updated sequential pattern trees (FUSP...
In this paper, the structure of the prelarge tree is proposed to maintain association rules for record modification based on the concept of pre-large itemsets. Due to the properties of the pre-large concept, the proposed algorithm can achieve a good execution time for tree maintenance especially when each time a small number of records are modified. Experimental results show that the proposed prelarge-tree...
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