Local support-based partition algorithm for frequent pattern mining

Kadappa, Vijayakumar; Nagesh, Shivaraju

doi:10.1007/s10044-018-0752-x

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…Local support counts were computed and global support count for the itemsets were determined from the local support counts. The experimental evaluation showed that LSPA outperformed Apriori, Partition, and FP-Tree algorithms in terms of database access time [24].…”

Section: Related Workmentioning

confidence: 99%

WBIN-Tree: A Single Scan Based Complete, Compact and Abstract Tree for Discovering Rare and Frequent Itemset Using Parallel Technique

Rai,

Kumar,

Shetty

et al. 2024

IEEE Access

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Data analytics is an integral part of strategic decision making in various fields but not limited to business, education, and healthcare systems. Existing research works focus on the discovery of itemsets with rare antecedents and consequent or frequent antecedents and consequent. Analysis of association among itemsets with rare antecedents and frequent consequent is equally important to gain valuable insights before making crucial decisions. Mining these itemsets from large datasets is time and resource intensive process. Expedition in the process of mining aids in quick decision making and hence, the entire dataset needs to be stored in the RAM. In this paper, a novel Weighted Binary Count Tree (WBIN-Tree) is proposed and implemented in CUDA to exploit the power of GPU and discover itemsets with rare antecedent and frequent consequent using parallel approach. WBIN-Tree stores the entire dataset in an abstract, complete, and compact form in the RAM using single database scan. WBIN-Tree is compared with existing sequential and parallel algorithms by varying the data size and dimension. The performance evaluation of WBIN-Tree showed promising results, proving to be the most time and space efficient algorithm to store the entire large dataset in the RAM. However, based on the size of the GPU, the performance drops when executed on datasets with large dimensions which could be handled by processing the attributes in batches. Additionally, a case study is included to understand the importance of mining association rules with rare antecedent and frequent consequent by executing the algorithm on breast cancer dataset.

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Section: Related Workmentioning

confidence: 99%

WBIN-Tree: A Single Scan Based Complete, Compact and Abstract Tree for Discovering Rare and Frequent Itemset Using Parallel Technique

Rai,

Kumar,

Shetty

et al. 2024

IEEE Access

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“…Limited efforts made to utilize the multicore feature of current processors LSPA [21] Proposed a partition-based approach that compares the local support against the global support to generate the resultant set of frequent items May classify some frequent items as infrequent and vice-versa PEMA [22]  Uses a combination of horizontal and vertical partitioning techniques  Mobile ARM agents incrementally integrate the locally mined frequent itemsets to produce the global set of frequent items  Relies on mobile agents for assimilating the results adding to the overall cost of the algorithm  Novel approaches such as multi-threading have not been employed here Adaptive-Miner [24] Makes execution plans before every iteration and selects the plan that minimizes time and space complexity Complex to design in terms of hardware and software required for communication PFIMD [28]  Uses DiffNodeset to increase the cardinality of N-list.  A 2-way comparison strategy is designed to reduce the time complexity of the algorithm Limited improvement in memory since DiffNodesets is not a very compact data structure.…”

Section: Pss-fim [18]mentioning

confidence: 99%

“…The frequent itemsets are then mined from each of the partitions concurrently. Kadappa and Nagesh [21] proposed an approach that involves dividing the dataset into as many partitions that are large enough to fit into the memory. For each partition, all those itemsets that satisfy the local support count are extracted.…”

Section: Introductionmentioning

confidence: 99%

A multithreaded hybrid framework for mining frequent itemsets

Poovan

Acharya

Reddy³

2022

IJECE

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<p><span>Mining frequent itemsets is an area of data mining that has beguiled several researchers in recent years. Varied data structures such as Nodesets, DiffNodesets, NegNodesets, N-lists, and Diffsets are among a few that were employed to extract frequent items. However, most of these approaches fell short either in respect of run time or memory. Hybrid frameworks were formulated to repress these issues that encompass the deployment of two or more data structures to facilitate effective mining of frequent itemsets. Such an approach aims to exploit the advantages of either of the data structures while mitigating the problems of relying on either of them alone. However, limited efforts have been made to reinforce the efficiency of such frameworks. To address these issues this paper proposes a novel multithreaded hybrid framework comprising of NegNodesets and N-list structure that uses the multicore feature of today’s processors. While NegNodesets offer a concise representation of itemsets, N-lists rely on List intersection thereby speeding up the mining process. To optimize the extraction of frequent items a hash-based algorithm has been designed here to extract the resultant set of frequent items which further enhances the novelty of the framework.</span></p>

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“…number of records, q = 2 d − 1 is the total item set count, d is the unique item count, and w is the maximum record length [34]. On the other hand, the time complexity of the FP-growth algorithm is O(n.d 2 ) , where n is the number of records and d is the number of unique items [35].…”

Section: Mtarm: Multitask Association Rule Minermentioning

confidence: 99%

Multitask-based association rule mining

Taşer¹,

Birant²,

Birant³

2020

Turk J Elec Eng & Comp Sci

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Recently, there has been a growing interest in association rule mining (ARM) in various fields. However, standard ARM algorithms fail to discover rules for multitask problems as they do not consider task-oriented investigation and, therefore, they ignore the correlation among the tasks. Considering this situation, this paper proposes a novel algorithm, named multitask association rule miner (MTARM), that tends to jointly discover rules by considering multiple tasks. This paper also introduces two novel concepts: single-task rule and multiple-task rule. In the first phase of the proposed approach, highly frequent local rules (single-task rules) are explored for each task separately and then these local rules are combined to produce the global result (multitask rules) using a majority voting mechanism. Experiments were conducted on four different real-world multitask learning datasets. The experimental results indicated that the proposed MTARM approach discovers more information than that of traditional ARM algorithms by jointly considering the relationships among multiple tasks. This work is licensed under a Creative Commons Attribution 4.0 International License. 933 YILDIRIM TAŞER et al./Turk J Elec Eng & Comp Scisubset of them, are familiar to one mutual rule set with a small difference. The proposed approach can discover common association rules by responding to different tasks and by applying an algorithm to consider all the tasks collectively. Accordingly, the rule X → Y can have high support and confidence in a task. A rule may not be frequent in the entire dataset; however, it may be frequent in several specific tasks. Therefore, finding task-based frequent rules in an effective way is important. This paper proposes a novel algorithm, named multitask association rule miner (MTARM), that tends to discover rules by jointly considering multiple tasks. The proposed algorithm mines datasets that include task information. The MTARM approach consists of two phases. In the first phase, the algorithm discovers local frequent rules (single-task rules) from the data of each task separately, and in the second phase, it combines these local rules to produce the global result (multitask rules). The discovered multitask rules can be used to deploy information systems that support the execution of their associated task or to improve decision-making in applications.The main contributions and novelty of this study are threefold: (i) this is the first study that applies ARM to multitask problems; (ii) it proposes the MTARM approach, which attempts to find intertask rules based solely on the task identities and the observed data for each task; and, finally (iii) it also introduces two novel concepts: single-task rule and multitask rule. This paper also provides a brief survey of ARM as it is an active research area of data mining. In addition, it presents the results of experimental studies conducted on four different real-world datasets to demonstrate the capability of the proposed algorithm.The rest of the paper is organized...

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Local support-based partition algorithm for frequent pattern mining

Cited by 7 publications

References 17 publications

WBIN-Tree: A Single Scan Based Complete, Compact and Abstract Tree for Discovering Rare and Frequent Itemset Using Parallel Technique

WBIN-Tree: A Single Scan Based Complete, Compact and Abstract Tree for Discovering Rare and Frequent Itemset Using Parallel Technique

A multithreaded hybrid framework for mining frequent itemsets

Multitask-based association rule mining

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