Fast algorithms for frequent itemset mining using FP-trees

Grahne, Gösta; Zhu, Jun

doi:10.1109/tkde.2005.166

Cited by 477 publications

(282 citation statements)

References 13 publications

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“…Pattern mining has widely used applications in a lot of areas such as association rule mining [4,13,18,24], sequence mining [19,21], and others [3,14]. Association rule mining is to mine relationships among items in a transaction database.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

Updating mined class association rules for record insertion

Nguyen

2014

Appl Intell

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“…For this work, mining frequent itemset, researched first by Agrawal et al [1] in 1993, has become more and more important and many new algorithms or improvements have been proposed to solve the problem more efficiently, such as Eclat [36], FP-Growth [18], FPGrowth* [14], BitTable-FI [12] and Index-BitTableFI [29].…”

Section: Introductionmentioning

confidence: 99%

Structure of frequent itemsets with extended double constraints

Tin

Hải

Ngan

2016

Vietnam J Comput Sci

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Frequent itemset discovering has been one essential task in data mining. In the worst case, the cardinality of the class of all frequent itemsets is of exponent which leads to many difficulties for users. Therefore, a model of constraintbased mining is necessary when their needs and interests are the top priority. This paper aims to find a structure of frequent itemsets that satisfy the following conditions: they include a subset C 10 , contain no items of a subset C 11 , and have at least an item belonging to subset C 21 . The first new point of the paper is the proposed theoretical result that is the generalization of our former researches (Hai et al. in Adv Comput Methods Knowl Eng Sci 479:367-378, 2013). Second, based on new sufficient and necessary conditions discovered just for closed itemsets and their generators in association with the methods of creating borders and eliminating branches and nodes on the lattice, we can effectively and quickly eliminate not only a class of frequent itemsets but also one or more branches of equivalence classes of which elements are insatiate the constraints. Third, a structure and a unique representation of frequent itemsets with extended double constraints are shown by representative closed itemsets and their generators. Finally, all theoretical results in this paper are proven to be reliable and they are firm bases to guarantee the correctness and efficiency of a new algorithm, MFS-EDC, which is used to effectively mine all constrained frequent itemsets. Experiments show the outstanding efficiency of this

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“…Recently, many systems are proposed for knowledge discovery from compressed databases [9]- [11] that start by transform the original database into a new data representation where several transactions are merged to become a new transaction then it uses an Apriori-like algorithm for association rule mining to find useful information. However, there are some problems in the approach suggested by M. Ashrafi et al [9]; first, the compressed database is not reversible after the original database is transformed by the data preprocessing step.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Compression Algorithm for Uncertain Databases Aimed at Mining Problems

Gabr¹,

Darwish²,

Mohsin³

2015

LNSE

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Abstract-Many studies on association rule mining have focused on item sets from precise data in which the presence and absence of items in transactions was certainly known. In some applications, the presence and absence of items in transactions are uncertain and the knowledge discovered from this type of data will extracted with approximation manner. Data compression offers a good solution to reduce data size that can save the time of discovering useful knowledge. In this paper we suggest a new algorithm to compress transactions from uncertain database based on modified version of M 2 TQT (Mining Merged Transactions with the Quantification Table) approach and fuzzy logic concept. The algorithm bands the uncertain data to set of clusters using K-Mean algorithm and exploits fuzzy membership function to classify the transaction items as one of those clusters. Finally, the modified version of M 2 TQT has been employed to compress the classified transactions. The key idea of our algorithm is that since uncertain data is probabilistic in nature and frequent item set is counted as expected values so, compressed transactions will give us approximate values for the item set's support. Experimental results show that the proposed algorithm is better than U-Apriori algorithm in case of large uncertain database.Index Terms-Rule mining, database compression, Uncertain database, fuzzy logic.

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Fast algorithms for frequent itemset mining using FP-trees

Cited by 477 publications

References 13 publications

Updating mined class association rules for record insertion

Updating mined class association rules for record insertion

Structure of frequent itemsets with extended double constraints

An Efficient Compression Algorithm for Uncertain Databases Aimed at Mining Problems

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