R-Apriori

Rathee, Sanjay; Kaul, Manohar; Kashyap, Arti

doi:10.1145/2809890.2809893

Cited by 50 publications

(2 citation statements)

References 11 publications

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“…Phase III concentrates on pattern mining and scoring these mined patterns. This step uses the distributed apriori algorithm Rathee et al (2015) to extract superset patterns, which occur frequently, and scores them based on their length, frequency, and whether they appear in the positive and negative classes. The overall score of a pattern is calculated by its score in the positive or negative class divided by the total score in both classes.…”

Section: Workflowmentioning

confidence: 99%

DILIC: An AI-Based Classifier to Search for Drug-Induced Liver Injury Literature

et al. 2022

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Drug-induced liver injury (DILI) is a class of adverse drug reactions (ADR) that causes problems in both clinical and research settings. It is the most frequent cause of acute liver failure in the majority of Western countries and is a major cause of attrition of novel drug candidates. Manual trawling of the literature is the main route of deriving information on DILI from research studies. This makes it an inefficient process prone to human error. Therefore, an automatized AI model capable of retrieving DILI-related articles from the huge ocean of literature could be invaluable for the drug discovery community. In this study, we built an artificial intelligence (AI) model combining the power of natural language processing (NLP) and machine learning (ML) to address this problem. This model uses NLP to filter out meaningless text (e.g., stop words) and uses customized functions to extract relevant keywords such as singleton, pair, and triplet. These keywords are processed by an apriori pattern mining algorithm to extract relevant patterns which are used to estimate initial weightings for a ML classifier. Along with pattern importance and frequency, an FDA-approved drug list mentioning DILI adds extra confidence in classification. The combined power of these methods builds a DILI classifier (DILIC), with 94.91% cross-validation and 94.14% external validation accuracy. To make DILIC as accessible as possible, including to researchers without coding experience, an R Shiny app capable of classifying single or multiple entries for DILI is developed to enhance ease of user experience and made available at https://researchmind.co.uk/diliclassifier/. Additionally, a GitHub link (https://github.com/sanjaysinghrathi/DILI-Classifier) for app source code and ISMB extended video talk (https://www.youtube.com/watch?v=j305yIVi_f8) are available as supplementary materials.

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Section: Workflowmentioning

confidence: 99%

DILIC: An AI-Based Classifier to Search for Drug-Induced Liver Injury Literature

et al. 2022

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“…With the widespread use of Apache Spark 9 platform, several approaches have been proposed to accelerate FIM algorithms via the memory‐based workflow engine of Spark, such as YAFIM 22 and R‐Apriori 23 . YAFIM uses three phases to realize Apriori based on RDD, 24 and extensive experiments illustrate that YAFIM outperforms the MapReduce‐based method.…”

Section: Related Workmentioning

confidence: 99%

On a parallel spark workflow for frequent itemset mining based on array prefix‐tree

Niu

et al. 2021

Concurrency and Computation

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Extracting frequent itemsets from datasets is an important problem in data mining, for which several mining methods including FP-Growth have been proposed. FP-Growth is a classical frequent itemset mining method, which generates pattern databases without candidates. Many improvements have been made in the literature due to the high time complexity and memory usage of FP-Growth. However, most of them still suffer from performance issues on large datasets. In this paper, we design an auxiliary structure, Array Prefix-Tree (AP-Tree), and propose a new algorithm, Array Prefix-Tree Growth (APT-Growth), which is further parallelized as a Spark workflow, referred to as PAPT-Growth. Based on a density threshold, we incorporate an adaptive algorithm selection process into PAPT-Growth to ensure its running time performance. We conduct extensive experiments on different thresholds and multiple datasets, and experimental results show the performance superiority of PAPT-Growth in comparison with several state-of-the-art methods such as PFP, YAFIM, and DFPS. The analysis on density reveals a changing point, which justifies the necessity and validity of adaptive algorithm selection.

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Collaborative filtering and association rule mining‐based market basket recommendation on spark

Wang

Wen

Guo

et al. 2019

Concurrency and Computation

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Summary Traditional market basket recommendation approaches normally cannot well recommend unpopular commodities in big data environment. To address such problem and deal with large datasets of practical supermarkets, this paper presents a market basket recommendation framework and proposes an Extended algorithm based on Collaborative Filtering and Association Rule mining, named ECFAR. The ECFAR covers two sub‐algorithms. First, a parallel FP‐Growth algorithm is used for mining association rules on Spark, which is designed to increase the efficiency of processing big data. Then, a parallel similar commodity discovery method based on matrix factorization is proposed. By analyzing a real‐world sales dataset collected from a local supermarket group, extensive experiments are conducted to verify its effectiveness.

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R-Apriori

Cited by 50 publications

References 11 publications

DILIC: An AI-Based Classifier to Search for Drug-Induced Liver Injury Literature

DILIC: An AI-Based Classifier to Search for Drug-Induced Liver Injury Literature

On a parallel spark workflow for frequent itemset mining based on array prefix‐tree

Collaborative filtering and association rule mining‐based market basket recommendation on spark

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