The Pre-FUFP algorithm for incremental mining

Lin, Chun–Wei; Hong, Tzung-Pei; Lu, Wen-Hsiang

doi:10.1016/j.eswa.2008.03.014

Cited by 105 publications

(54 citation statements)

References 15 publications

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“…7,8,9,10,11,12,13,14,15,16,17,18,19,to 20 show that CAR-Incre is more efficient than CAR-Miner in most cases, especially in large datasets or large minSup. Examples are Poker-hand (a large number of records) or Chess (minSup is large).…”

Section: Resultsmentioning

confidence: 99%

“…In 2009, Lin et al proposed the Pre-FUFP algorithm for mining frequent itemsets in a dataset by combining the FPtree and the pre-large concept [11]. They proposed an algorithm that updates the FP-tree when a new dataset is inserted using the safety threshold f .…”

Section: Mining Association Rules From Incremental Datasetsmentioning

confidence: 99%

“…The concept of pre-large itemsets (i.e. itemsets which do not satisfy the minimum support threshold, but satisfy the lower minimum support threshold) is applied to avoid re-scanning the original dataset [5,11]. 2.…”

Section: Introductionmentioning

confidence: 99%

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Updating mined class association rules for record insertion

Nguyen

2014

Appl Intell

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Mining class association rules is an interesting problem in classification and prediction. Some recent studies have shown that using classifiers based on class association rules resulted in higher accuracy than those obtained by using other classification algorithms such as C4.5 and ILA. Although many algorithms have been proposed for mining class association rules, they were used for batch processing. However, real-world datasets regularly change; thus, updating a set of rules is challenging. This paper proposes an incremental method for mining class association rules when records are inserted into the dataset. Firstly, a modified equivalence class rules tree (MECR-tree) is created from the original dataset. When records are inserted, nodes on the tree are updated by changing their information including Obidset (a set of object identifiers containing the node's itemset), count, and pos. Secondly, the concept of pre-large itemsets is applied to avoid re-scanning the original dataset. Finally, a theorem is proposed to quickly prune nodes that cannot generate rules in the tree update process. Experimental results show that the proposed method is more effective than mining entire original and inserted datasets.

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Section: Resultsmentioning

confidence: 99%

Section: Mining Association Rules From Incremental Datasetsmentioning

confidence: 99%

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Updating mined class association rules for record insertion

Nguyen

2014

Appl Intell

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“…Besides, we will apply our method for mining weighted utility association rules [9]. The mining of association rules in incremental databases has also been considered in recent years [3][4][5][6]12]. The intention is thus to also consider the concept of mining weighted association rules from such incremental databases.…”

Section: Discussionmentioning

confidence: 99%

A new method for mining Frequent Weighted Itemsets based on WIT-trees

Vo¹,

Coenen

2013

Expert Systems with Applications

114

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Abstract:The mining frequent itemsets plays an important role in the mining of association rules. Frequent itemsets are typically mined from binary databases where each item in a transaction may have a different significance. Mining Frequent Weighed Itemsets (FWI) from weighted items transaction databases addresses this issue. This paper therefore proposes algorithms for the fast mining of FWI from weighted item transaction databases. Firstly, an algorithm for directly mining FWI using WIT-trees is presented. After that, some theorems are developed concerning the fast mining of FWI.Based on these theorems, an advanced algorithm for mining FWI is proposed. Finally, a Diffset strategy for the efficient computation of the weighted support for itemsets is described, and an algorithm for mining FWI using Diffsets presented. A complete evaluation of the proposed algorithms is also presented.

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“…The designed FP-growth algorithm is then performed recursively by the produced conditional FP-tree to mine the frequent itemsets. Since FP-tree-like structure is more effective than the Apriori-like way for mining frequent itemsets or association rules, most approaches based on the FP-tree-like structure are still developed in progress [1,11,16,25].…”

Section: Fup Conceptmentioning

confidence: 99%

Updating high-utility pattern trees with transaction modification

Lin

Zhang

Gan

et al. 2014

Multimed Tools Appl

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Traditional association-rule mining only concerns the occurrence frequencies of the items in a binary database. In real-world applications, customers may buy several copies of the purchased items. Other factors such as profit, quantity, or price should be concerned to measure the utilities of the purchased items. High-utility itemsets mining was thus proposed to consider the factors of quantity and profit. Two-phase model was the most commonly way Multimed Tools Appl to keep the transaction-weighted utilization downward closure property, thus reducing the numerous candidates in utility mining. Most methods for finding high-utility itemsets are used to handle a static database. In practical applications, transactions are changed whether insertion, deletion, or modification. Some itemsets may arise as the new high-utility itemsets or become invalid knowledge in the updated database. In this paper, a maintenance Fast Updated High Utility Pattern tree for transaction MODification (FUP-HUP-tree-MOD) algorithm is thus proposed to effective maintain and update the built HUP tree for mining high-utility itemsets in dynamic databases without candidate generation. Experiments are conducted to show better performance of the proposed algorithm compared to the two-phase algorithm and the HUP tree algorithm in batch mode.

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The Pre-FUFP algorithm for incremental mining

Cited by 105 publications

References 15 publications

Updating mined class association rules for record insertion

Updating mined class association rules for record insertion

A new method for mining Frequent Weighted Itemsets based on WIT-trees

Updating high-utility pattern trees with transaction modification

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