A transaction mapping algorithm for frequent itemsets mining

Ming, Song; Rajasekaran, Sanguthevar

doi:10.1109/tkde.2006.1599386

Cited by 73 publications

(35 citation statements)

References 6 publications

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“…Transaction Mapping (TM) (Song and Rajasekaran 2006) is an algorithm that mines frequent itemsets using the vertical data representation like the EClaT algorithm. In this algorithm, the transaction IDs of every itemset are transformed and mapped into a list of transaction intervals at another location.…”

Section: Tm Algorithmmentioning

confidence: 99%

Algorithms for frequent itemset mining: a literature review

Chee¹,

Jaafar²,

Aziz³

et al. 2018

Artif Intell Rev

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Data Analytics plays an important role in the decision making process. Insights from such pattern analysis offer vast benefits, including increased revenue, cost cutting, and improved competitive advantage. However, the hidden patterns of the frequent itemsets become more time consuming to be mined when the amount of data increases over the time. Moreover, significant memory consumption is needed in mining the hidden patterns of the frequent itemsets due to a heavy computation by the algorithm. Therefore, an efficient algorithm is required to mine the hidden patterns of the frequent itemsets within a shorter run time and with less memory consumption while the volume of data increases over the time period. This paper reviews and presents a comparison of different algorithms for Frequent Pattern Mining (FPM) so that a more efficient FPM algorithm can be developed.

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Section: Tm Algorithmmentioning

confidence: 99%

Algorithms for frequent itemset mining: a literature review

Chee¹,

Jaafar²,

Aziz³

et al. 2018

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

“…So, this method drastically reduces the size of memory required by orders of magnitude to store intermediate results that significantly increase the performance. Mingjun Song, and Sanguthevar Rajasekaran recommended a novel Algorithm called Transaction Mapping Algorithm by exploring the vertical data representation for Frequent Itemsets Mining [12]. The transaction tree is used to represent all the transactions in the database.…”

Section: Related Workmentioning

confidence: 99%

Coalesce based binary table: an enhanced algorithm for mining frequent patterns

Moturi¹,

Vemuru²,

Tirumalarao³

2017

IJET

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Frequent item set mining and association rule mining is the key tasks in knowledge discovery process. Various customized algorithms are being implemented in Association Rule Mining process to find the set of frequent patterns. Though we have many algorithms apriori is one of the standard algorithm for finding frequent itemsets, but this algorithm is inefficient because of several scans of database and more number of candidates to be generated. To overcome these limitations, in this paper a new algorithm called Coalesce based Binary Table is introduced. Through this algorithm the given database is scanned only once to generate Binary Table by which frequent-1 itemsets are found. To progress the process, infrequent-1 itemsets are identified and removed from the Binary Table to rearrange the items in support ascending order. To each frequent-1 itemset find Coalesce matrix and Index List to generate all frequent itemsets having the same support count as representative items and the remaining frequent itemsets are obtained in depth first manner. The significant benefits with the proposed method are the whole database is scanned only once, no need to generate and check each candidate to find the set of frequent items. On the other hand frequent items having the same support counts as representative items can be identified directly by joining the representative item with all the combinations of Coalesce matrix. So, it is proven that coalesce based Binary Table is panacea to cut short the time in identifying the frequent itemsets hence the efficiency is improved.

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“…It never bothers with the support count formalities, unlike the previous methods available. An algorithm called transaction mapping (TM) [22] was proposed, which uses the transaction IDs for mapping item sets and they are compressed for mining. All transactions from the tree are mapped to an interval list, which is stored by the nodes of the tree.…”

Section: Review Of Existing Workmentioning

confidence: 99%

SOTARM: Size of transaction-based association rule mining algorithm

Pandian¹,

Thaveethu²

2017

Turk J Elec Eng & Comp Sci

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Abstract:Mining of association rules tries to identify the existence of promising and fruitful relations among the items present in a database. The basic a priori algorithm suffers from multiple database scans, and if the database is large, then the time taken for scanning and generation of candidates is also large. The proposed algorithm attempts to reduce the repeated scanning of the whole database. Using this algorithm, scanning time and also the generation of subitems that are not frequent can be reduced. The former can be done by sorting the transaction records in descending order based on the size of transaction (SOT) and scanning only those transactions whose SOT is greater than or equal to k (size of item sets). The latter can be done by analyzing the item set state. It is not required to generate the next set of candidate item sets using those item sets that are not frequent. Both positive and negative mining has been done in R Studio of the R data mining tool using the R language. Experimental results show that the SOT algorithm performs better than the Apriori, Eclat, PVARM (partition-based validation for association rule mining), and NRRM (nonredundant rule method) algorithms. The work has been tested against various standard datasets such as Adult, Genome, Groceries, and SER (State Electricity Rate) Prediction. The speed-up and efficiency parameter values obtained from the algorithm strongly suggest that the proposed SOTARM algorithm has attained better performance when compared to all the other existing algorithms.

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A transaction mapping algorithm for frequent itemsets mining

Cited by 73 publications

References 6 publications

Algorithms for frequent itemset mining: a literature review

Algorithms for frequent itemset mining: a literature review

Coalesce based binary table: an enhanced algorithm for mining frequent patterns

SOTARM: Size of transaction-based association rule mining algorithm

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