Mining Positive and Negative Association Rule from Frequent and Infrequent Pattern based on IMLMS_GA

Rai, Nikky; Jain, Susheel; Jain, Anurag

doi:10.5120/13555-1393

Cited by 3 publications

(4 citation statements)

References 12 publications

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“…W 1 and W 2 are weights. By swapping out certain transactions for their offspring that have the most accessible data items, the system reduces the amount of lost rules and ghost rules [46][47][48][49]. Additional details on the use of machine learning methods for privacy-preserving data mining in IoTbased healthcare applications and vehicular cloud network settings are provided in the most recent references [50][51][52][53][54][55][56][57][58][59].…”

Section: Genetic Algorithm-based Data Sanitizationmentioning

confidence: 99%

An Adaptive Privacy Preserving Framework for Distributed Association Rule Mining in Healthcare Databases

Kuba¹,

Azzawi²,

Darwish³

et al. 2023

Computers, Materials &Amp; Continua

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It is crucial, while using healthcare data, to assess the advantages of data privacy against the possible drawbacks. Data from several sources must be combined for use in many data mining applications. The medical practitioner may use the results of association rule mining performed on this aggregated data to better personalize patient care and implement preventive measures. Historically, numerous heuristics (e.g., greedy search) and metaheuristics-based techniques (e.g., evolutionary algorithm) have been created for the positive association rule in privacy preserving data mining (PPDM). When it comes to connecting seemingly unrelated diseases and drugs, negative association rules may be more informative than their positive counterparts. It is well-known that during negative association rules mining, a large number of uninteresting rules are formed, making this a difficult problem to tackle. In this research, we offer an adaptive method for negative association rule mining in vertically partitioned healthcare datasets that respects users' privacy. The applied approach dynamically determines the transactions to be interrupted for information hiding, as opposed to predefining them. This study introduces a novel method for addressing the problem of negative association rules in healthcare data mining, one that is based on the Tabu-genetic optimization paradigm. Tabu search is advantageous since it removes a huge number of unnecessary rules and item sets. Experiments using benchmark healthcare datasets prove that the discussed scheme outperforms state-of-the-art solutions in terms of decreasing side effects and data distortions, as measured by the indicator of hiding failure.

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Section: Genetic Algorithm-based Data Sanitizationmentioning

confidence: 99%

An Adaptive Privacy Preserving Framework for Distributed Association Rule Mining in Healthcare Databases

Kuba¹,

Azzawi²,

Darwish³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…The fitness function should be tailored to the particular search areas; consequently, the fitness function used is critical for obtaining the required results. It is well established that the rule is more effective when 𝑁𝑁 𝑎𝑎 and 𝑁𝑁 𝑑𝑑 are greater than 𝑁𝑁 𝑏𝑏 and 𝑁𝑁 𝑐𝑐 [42] [46].…”

Section: Phase 1: Extracting Optimized Negative Rulesmentioning

confidence: 99%

“…𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 = {𝑁𝑁 𝑎𝑎 /(𝑁𝑁 𝑎𝑎 + 𝑁𝑁 𝑏𝑏 )} (5) Fitness = (CF * 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶) (6) In this scenario, the system encodes the rules using the same form as in [46]. The best of the new solutions is then chosen for further testing using Tabu restrictions and aspiration criteria.…”

Section: Phase 1: Extracting Optimized Negative Rulesmentioning

confidence: 99%

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Privacy Preserving Data Mining Framework for Negative Association Rules: An Application to Healthcare Informatics

Darwish

Essa²,

Osman³

et al. 2022

IEEE Access

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Protecting the privacy of healthcare information is an important part of encouraging data custodians to give accurate records so that mining may proceed with confidence. The application of association rule mining in healthcare data has been widespread to this point in time. Most applications focus on positive association rules, ignoring the negative consequences of particular diagnostic techniques. When it comes to bridging divergent diseases and drugs, negative association rules may give more helpful information than positive ones. This is especially true when it comes to physicians and social organizations (e.g., a certain symptom will not arise when certain symptoms exist). Data mining in healthcare must be done in a way that protects the identity of patients, especially when dealing with sensitive information. However, revealing this information puts it at risk of attack. Healthcare data privacy protection has lately been addressed by technologies that disrupt data (data sanitization) and reconstruct aggregate distributions in the interest of doing research in data mining. In this study, metaheuristic-based data sanitization for healthcare data mining is investigated in order to keep patient privacy protected. It is hoped that by using the Tabu-genetic algorithm as an optimization tool, the suggested technique chooses item sets to be sanitized (modified) from transactions that satisfy sensitive negative criteria with the goal of minimizing changes to the original database. Experiments with benchmark healthcare datasets show that the suggested privacy preserving data mining (PPDM) method outperforms existing algorithms in terms of Hiding Failure (HF), Artificial Rule Generation (AR), and Lost Rules (LR).

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Classification of Documents using Effective Pattern Taxonomy

Kiran¹,

Ravikanth²

2014

IJCA

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Text mining is a technique helps users in extracting useful information from large amount of database available digitally on web or text data. Pattern Taxonomy based model containing sequential pattern used to perform the task. EPT (Effective Pattern Taxonomy) method helps in extracting useful patterns in the text documents by classifying them in Positive and Negative documents. Pattern-based method outperforms keyword based methods. Pattern based method is best way to eliminate meaningless as well as closed sequential patterns thus saving computational time and increases effectiveness of the system.

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Mining Positive and Negative Association Rule from Frequent and Infrequent Pattern based on IMLMS_GA

Cited by 3 publications

References 12 publications

An Adaptive Privacy Preserving Framework for Distributed Association Rule Mining in Healthcare Databases

An Adaptive Privacy Preserving Framework for Distributed Association Rule Mining in Healthcare Databases

Privacy Preserving Data Mining Framework for Negative Association Rules: An Application to Healthcare Informatics

Classification of Documents using Effective Pattern Taxonomy

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