CL-MAX: a clustering-based approximation algorithm for mining maximal frequent itemsets

Fatemi, Seyed Mohsen; Hosseini, Seyed Mohsen; Kamandi, Ali; Shabankhah, Mahmood

doi:10.1007/s13042-020-01177-5

Cited by 8 publications

(10 citation statements)

References 29 publications

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“…Approximate itemset miners (such MODL purports to be) are proposed as a solution ( 25 ). Indeed, when using a clustering-based approximate itemset miner (CL-MAX, ( 30 )), results that are close to MODL’s. However, CL-MAX is more vulnerable to a poor choice of hyperparameters.…”

Section: Resultsmentioning

confidence: 86%

OLOGRAM-MODL: mining enrichedn-wise combinations of genomic features with Monte Carlo and dictionary learning

Ferré

Capponi

Puthier

2021

NAR Genomics and Bioinformatics

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Most epigenetic marks, such as Transcriptional Regulators or histone marks, are biological objects known to work together in n-wise complexes. A suitable way to infer such functional associations between them is to study the overlaps of the corresponding genomic regions. However, the problem of the statistical significance of n-wise overlaps of genomic features is seldom tackled, which prevent rigorous studies of n-wise interactions. We introduce OLOGRAM-MODL, which considers overlaps between n ≥ 2 sets of genomic regions, and computes their statistical mutual enrichment by Monte Carlo fitting of a Negative Binomial distribution, resulting in more resolutive P-values. An optional machine learning method is proposed to find complexes of interest, using a new itemset mining algorithm based on dictionary learning which is resistant to noise inherent to biological assays. The overall approach is implemented through an easy-to-use CLI interface for workflow integration, and a visual tree-based representation of the results suited for explicability. The viability of the method is experimentally studied using both artificial and biological data. This approach is accessible through the command line interface of the pygtftk toolkit, available on Bioconda and from https://github.com/dputhier/pygtftk

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Section: Resultsmentioning

confidence: 86%

OLOGRAM-MODL: mining enrichedn-wise combinations of genomic features with Monte Carlo and dictionary learning

Ferré

Capponi

Puthier

2021

NAR Genomics and Bioinformatics

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“…MFI mining concept was later introduced as in MAFIA [20], FP-Max [22], FP-Max * [23], PADS [25], SelPMiner [19], and CL-Max [26]. MAFIA is an MFI method, which uses a bitmap representation to check itemsets' support information without any database scan.…”

Section: Related Workmentioning

confidence: 99%

“…However, it is memory-consuming due to its storing conditional databases and time-consuming due to longer search space [25]. CL-Max is an algorithm, which uses k-means concept for MFI mining [26]. SelPMiner was introduced to utilize the optimizations of the search space pruning through itemset-count tree format [19].…”

Section: Related Workmentioning

confidence: 99%

“…MFI set is considerably smaller than that of FIs [19]. ere are many competent algorithms used for mining MFIs such as MAFIA [20], GenMax [21], FP-Max [22], FP-Max * [23], Charm-MFI [24], PADS [25], SelPMiner [19], and CL-Max [26]. e FP-Max [22] and FP-Max * [23] algorithms use FP-Growth to build a tree as FPtree and mine MFIs from the tree based on a bottom-up and divide-and-rule strategy.…”

Section: Introductionmentioning

confidence: 99%

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An Incremental Interesting Maximal Frequent Itemset Mining Based on FP‐Growth Algorithm

Alsaeedi

Al-Hegami

2022

Complexity

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Frequent itemset mining is the most important step of association rule mining. It plays a very important role in incremental data environments. The massive volume of data creates an imminent need to design incremental algorithms for the maximal frequent itemset mining in order to handle incremental data over time. In this study, we propose an incremental maximal frequent itemset mining algorithms that integrate subjective interestingness criterion during the process of mining. The proposed framework is designed to deal with incremental data, which usually come at different times. It extends FP-Max algorithm, which is based on FP-Growth method by pushing interesting measures during maximal frequent itemset mining, and performs dynamic and early pruning to leave uninteresting frequent itemsets in order to avoid uninteresting rule generation. The framework was implemented and tested on public databases, and the results found are promising.

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“…Fatemi et al 36 demonstrated CL‐MAX: a clustering‐based approximation algorithm for mining maximal frequent itemsets. Standard deviation, support ratio, execution time, and recalls were the performance measures employed in the evaluation of this approach.…”

Section: Review Of Related Workmentioning

confidence: 99%

Association rule mining based fuzzy manta ray foraging optimization algorithm for frequent itemset generation from social media

Lakshmi¹,

Krishnamurthy²

2021

Concurrency and Computation

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Nowadays, the concept of data mining is employed widely and created a great deal of attention due to its fast arrival. Numerous approaches to frequent itemsets and association rule mining (ARM) are exemplified in recent years, but still, the performances based on scalability and processing time are considered as a major drawback that results in obtaining the solutions with very poor quality. To overcome such shortcomings, this article proposes three significant phases, namely, the data pre-processing phase, data pre-processing, frequent itemset mining, and ARM. In data pre-processing phase, the collected twitter datasets are pre-processed to eliminate redundant data and convert them into an appropriate format for further mining. In the frequent itemset mining phase, an Apriori algorithm is employed for the exact mining of frequent itemsets. The ARM phase utilizes the fuzzy manta ray foraging (FMRF) optimization algorithm that involves the generation of association rules from the huge itemsets thereby achieving minimum confidence and minimum support value. Here, the recent tweets regarding Covid-19, trump2020, joebiden, draintheswamp, and Godzilla are the datasets collected from the Twitter web link. The experimental analysis and the comparative performances are performed for various simulation measures and the results reveal that the proposed approach provides effective performances when compared with various other existing approaches.

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CL-MAX: a clustering-based approximation algorithm for mining maximal frequent itemsets

Cited by 8 publications

References 29 publications

OLOGRAM-MODL: mining enrichedn-wise combinations of genomic features with Monte Carlo and dictionary learning

OLOGRAM-MODL: mining enrichedn-wise combinations of genomic features with Monte Carlo and dictionary learning

An Incremental Interesting Maximal Frequent Itemset Mining Based on FP‐Growth Algorithm

Association rule mining based fuzzy manta ray foraging optimization algorithm for frequent itemset generation from social media

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