negFIN: An efficient algorithm for fast mining frequent itemsets

Aryabarzan, Nader; Minaei-Bidgoli, Behrouz; Teshnehlab, Mohammad

doi:10.1016/j.eswa.2018.03.041

Cited by 77 publications

(44 citation statements)

References 33 publications

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“…This research suggested various SBRs in order to improve recommenders' performance. There are existing studies that focus on session information [34][35][36][37][38]. Our proposed SBR methods are unique compared to previous research because we consider item sessions as well as attribute sessions.…”

Section: Discussionmentioning

confidence: 99%

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Session-Based Recommender System for Sustainable Digital Marketing

Hwangbo

Kim

2019

Sustainability

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Many companies operate e-commerce websites to sell fashion products. Some customers want to buy products with intention of sustainability and therefore the companies need to suggest appropriate fashion products to those customers. Recommender systems are key applications in these sustainable digital marketing strategies and high performance is the most necessary factor. This research aims to improve recommendation systems’ performance by considering item session and attribute session information. We suggest the Item Session-Based Recommender (ISBR) and the Attribute Session-Based Recommenders (ASBRs) that use item and attribute session data independently, and then we suggest the Feature-Weighted Session-Based Recommenders (FWSBRs) that combine multiple ASBRs with various feature weighting schemes. Our experimental results show that FWSBR with chi-square feature weighting scheme outperforms ISBR, ASBRs, and Collaborative Filtering Recommender (CFR). In addition, it is notable that FWSBRs overcome the cold-start item problem, one significant limitation of CFR and ISBR, without losing performance.

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Section: Discussionmentioning

confidence: 99%

“…The Association Rules Recommender (ARR) is a representative approach among SBRs. Originally it was developed to discover user consumption patterns within a large transaction database regardless of the order of their appearance [34,35]. Later was extended to consider sequence patterns in the transactions [36][37][38].…”

Section: Session-based Recommender Systemsmentioning

confidence: 99%

Session-Based Recommender System for Sustainable Digital Marketing

Hwangbo

Kim

2019

Sustainability

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“…In this section, the algorithm HPrePostPlus was compared with its original version PrePost [8], three state-of-the art algorithms negFin [25], MRPrePost [18] and the well-known PFP [20]. We evaluated the speed performance by analyzing the running time and scalability.…”

Section: Methodsmentioning

confidence: 99%

“…Recently, three types of structure have been suggested for representing itemsets: Node-list [23], N-list [8], and Node set [24,25], to facilitate the mining of frequent itemsets. They are founded on a prefix coding tree, which save the sufficient information about frequent itemsets.…”

Section: Related Workmentioning

confidence: 99%

Performance Improvement of PrePost Algorithm Based on Hadoop for Big Data

Rochd¹,

Hafidi²

2018

IJIES

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With the blasting growth in data, uptake data mining techniques to mine association rules, and then find useful information hidden in large data has become ever more important. Several existing data mining techniques often through mining frequent itemsets draw association rules and get to relevant knowledge, but with the rapid arrival of the era of big data, traditional data mining algorithms have been impossible to meet large data's analysis needs. Lately, the PrePost algorithm has been suggested, a new algorithm for mining frequent itemsets based on the idea of N-lists. PrePost in most cases outperforms other present state-of-the-art algorithms. In mind of this, we present the HPrePostPlus algorithm. A better version of PrePost based on Hadoop, that utilization a HashMap to traverse effectively the PPC tree, and improve the process of creating the N-lists related with 1-itemsets. We combine also the characteristic of Hadoop with a view to process large data. Experience has demonstrated that HPrePostPlus algorithm is greater than the state-of-the-art methods in terms of performance and scalability.

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“…In 2018, we presented, HPrePostPlus algorithm [26], a better version of PrePost, based on Hadoop, which uses a HashMap to traverse efficiently through the PPC tree and enhance the N-list creation process. The HPrePostPlus algorithm is very powerful and surpasses the state-of-the-art algorithms, such as PrePost [6], MRPrePost [23], PFP [18], and negFIN [27]. Although N-list are effective structures for mining frequent itemsets, hey need to contain pre-order and post-order number, which is memory-consuming and inconvenient to mine frequent itemsets.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Distributed Frequent Itemset Mining Algorithm Based on Spark for Big Data

Rochd¹,

Hafidi²

2019

IJIES

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Frequent item exploration is a fundamental element in many data mining problems aimed at finding interesting models in the data. Recently, the PrePost algorithm, a new algorithm for extraction frequent element sets based on the idea of N-lists, which in most cases surpasses other current state-of-the-art algorithms, has been introduced. The PrePost algorithm's performance deteriorates when it comes to handling big data. Nevertheless, the current existing PrePost algorithms in place implemented with the MapReduce model are not sufficiently powerful for iterative computation. To reduce IO overhead and take advantage of cluster memory, this article offers an enhanced version of PrePost, the Distributed PrePost (DisPrePost), a parallel algorithm built on the Spark framework, which incorporates the concept of resilient distributed datasets and performs in-memory processing to optimize the execution time of operation, that also utilises a HashMap to further refine the N-list creation process. Experience has shown that the DisPrePost algorithm is more efficient and scalable than the two advanced state-of-the-art methods HPrePostPlus and the well-known algorithm HFIM.

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negFIN: An efficient algorithm for fast mining frequent itemsets

Cited by 77 publications

References 33 publications

Session-Based Recommender System for Sustainable Digital Marketing

Session-Based Recommender System for Sustainable Digital Marketing

Performance Improvement of PrePost Algorithm Based on Hadoop for Big Data

An Efficient Distributed Frequent Itemset Mining Algorithm Based on Spark for Big Data

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