Building FP-Tree on the Fly: Single-Pass Frequent Itemset Mining

Shahbazi, Nima; Soltani, Rohollah; Gryz, Jarek; An, Aijun

doi:10.1007/978-3-319-41920-6_30

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…Shahbazi et al designed SPFP-Tree using single database scan to discover frequent patterns and handle incremental datasets. However, the prefix-paths of the tree were restructured according based on two hash tables which introduces computation and memory overhead [22]. A a hash based technique was utilised in SS-FIM algorithm to store the power set of all items in the transaction with its count [23].…”

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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“…Items with their corresponding frequencies are shown in Table 3. (Huang et al, 2002) Addition 1 FP-tree Generalized FPtree, (Ezeife and Su, 2002) Addition  1 FP-tree CATS-tree, (Cheung and Zaiane, 2003) Addition, Deletion 1 FP-tree AFPIM, (Koh and Shieh, 2004) Addition, Deletion, Modification  0 FP-tree IFP-tree, (Adnan et al, 2006a) Addition, Deletion, Modification 2 FP-tree CanTree, (Leung et al, 2007) Addition, Deletion, Modification 0 FP-tree FUFP, (Hong et al, 2008) Addition, Deletion  1 FP-tree Pre-FUFP, (Lin et al, 2009) Addition, Deletion  0 FP-tree CP-tree, (Tanbeer et al, 2009) Addition, Deletion 1 FP-tree Improved CPtree, (Hamedanian et al, 2013) Addition  1 FP-tree GM-tree, (Roul and Bansal, 2014) Addition 1 FP-tree HUPIDGrowth, (Yun and Ryang, 2015) Addition, Deletion 1 FP-tree SPFPtree, (Shahbazi et al, 2016) Addition 1 FP-tree VSIFP, (Yu-Dong et al, 2016) Addition 1 FP-tree SSP-tree, (Borah and Nath, 2018) Addition, Deletion 1 FP-tree The items are then sorted with respect to their frequency count in descending order. Since the minimum support is 1, every item occurring at least once in the database will be considered to construct the complete Improved FP-tree.…”

Section: Improved Fp-tree Constructionmentioning

confidence: 99%

Is Single Scan based Restructuring Always a Suitable Approach to Handle Incremental Frequent Pattern Mining?

Ahmed¹,

Nath²

2021

Journal of Computer Science

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Incremental mining of frequent patterns has attracted the attention of researchers in the last two decades. The researchers have explored the frequent pattern mining from incremental database problems by considering that the complete database to be processed can be accommodated in systems' main memory even after the database gets updated very frequently. The FP-tree-based approaches were able to draw more interest because of their compact representation and requirement of a minimum number of database scans. The researchers have developed a few FP-tree based methods to handle the incremental scenario by adjusting or restructuring the tree prefix paths. Although the approaches have managed to solve the re-computation problem by constructing a complete pattern tree data structure using only one database scan, restructuring the prefix paths for each transaction is a computationally costly task, leading to the high tree construction time. If the FP-tree construction process can be supported with suitable data structures, reconstruction of the FP-tree from scratch may be less time consuming than the restructuring approaches in case of incremental scenario. In this study, we have proposed a tree data structure called Improved Frequent Pattern tree (Improved FP-tree). The proposed Improved FP-tree construction algorithm has immensely improved the performance of tree construction time by resourcefully using node links, maintained in header table to manage the same item node list in the FP-tree. The experimental results emphasize the significance of the proposed Improved FP-tree construction algorithm over a few conventional incremental FP-tree construction algorithms with prefix path restructuring.

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“…Many previous works [10], [26], [27], [28], [29], [30], [31], [33], [34], [35] have shown that pattern growth approaches based on prefix trees are more efficient than candidate generation-and-test approaches. However, pattern growth approaches are confronted with a problem that they have to spend much time in constructing many (conditional) prefix trees.…”

Section: A Motivationmentioning

confidence: 99%

Efficient Mining of Frequent Itemsets Using Only One Dynamic Prefix Tree

Hang

Zhao

et al. 2020

IEEE Access

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Frequent itemset mining is a fundamental problem in data mining area because frequent itemsets have been extensively used in reasoning, classifying, clustering, and so on. To mine frequent itemsets, previous algorithms based on a prefix tree structure have to construct many prefix trees, which is very time-consuming. In this paper, we propose a novel frequent itemset mining algorithm called DPT (Dynamic Prefix Tree) which uses only one prefix tree. We first introduce the concept of the postconditional database of an itemset, and analyze the distribution of an itemset's post-conditional database in a prefix tree representing a database. Subsequently, we illuminate how DPT adjusts the prefix tree to mine frequent itemsets and give three optimization techniques. An interesting advantage of DPT is that the algorithm can directly output a prefix tree representing all frequent itemsets after slight modifications. Using only one dynamic prefix tree, DPT avoids the high cost of constructing many prefix trees and thus gains significant performance improvement. Experimental results show that DPT remarkably outperforms previous algorithms with respect to running time and memory usage, and that a prefix tree representing all frequent itemsets DPT outputs can be used more efficient than a list representing them previous algorithms output.

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Building FP-Tree on the Fly: Single-Pass Frequent Itemset Mining

Cited by 5 publications

References 15 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

Is Single Scan based Restructuring Always a Suitable Approach to Handle Incremental Frequent Pattern Mining?

Efficient Mining of Frequent Itemsets Using Only One Dynamic Prefix Tree

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