BUFA |  Basketball |  Opt. Etc. |  (Mata et al., 2002; Alataş and Akin, 2006; Martínez-Ballesteros et al., 2011; Martín et al., 2014b; Martínez-Ballesteros et al., 2016; Martín et al., 2016; Moslehi and Haeri, 2020; Alatas et al., 2008; Almasi and Abadeh, 2015; Altay and Alatas, 2022; Alatas and Akin, 2008, 2009; Moslehi et al., 2011; Beiranvand et al., 2014; Kahvazadeh and Abadeh, 2015; Can and Alatas, 2017; Heraguemi et al., 2018; Tahyudin and Nambo, 2019; Kuo et al., 2019; Ledmi et al., 2021; Moslehi et al., 2020; Jaramillo et al., 2021; Minaei-Bidgoli et al., 2013)
Balance Scale |  Opt. Etc. |  (Martín et al., 2016, 2014b; Almasi and Abadeh, 2015; Hu et al., 2022)
Bolts |  Opt. |  (Mata et al., 2002; Alataş and Akin, 2006; Martínez-Ballesteros et al., 2011; Martín et al., 2014b, a; Martínez-Ballesteros et al., 2016; Martín et al., 2016; Altay and Alatas, 2021; Alatas et al., 2008; Almasi and Abadeh, 2015; Altay and Alatas, 2022; Alatas and Akin, 2008; Moslehi et al., 2011; Tahyudin and Nambo, 2019; Ledmi et al., 2021; Moslehi et al., 2020)
House_16H |  Opt. |  (Martín et al., 2014b, a; Martínez-Ballesteros et al., 2016; Martín et al., 2016; Altay and Alatas, 2021; Almasi and Abadeh, 2015; Luna et al., 2014)
Pollution |  Opt. |  (Mata et al., 2002; Alataş and Akin, 2006; Martínez-Ballesteros et al., 2011; Martín et al., 2014b, a; Martínez-Ballesteros et al., 2016; Martín et al., 2016; Altay and Alatas, 2021; Alatas et al., 2008; Almasi and Abadeh, 2015; Altay and Alatas, 2022; Alatas and Akin, 2008; Moslehi et al., 2011; Tahyudin and Nambo, 2019; Moslehi et al., 2020; Ledmi et al., 2021)
Quake |  Opt. |  (Mata et al., 2002; Alataş and Akin, 2006; Martínez-Ballesteros et al., 2011; Minaei-Bidgoli et al., 2013; Martín et al., 2014b; Martínez-Ballesteros et al., 2016; Martín et al., 2016; Moslehi and Haeri, 2020; Alatas et al., 2008; Almasi and Abadeh, 2015; Altay and Alatas, 2022; Martín et al., 2014a; Altay and Alatas, 2021; Alatas and Akin, 2008, 2009; Moslehi et al., 2011; Beiranvand et al., 2014; Kahvazadeh and Abadeh, 2015; Can and Alatas, 2017; Heraguemi et al., 2018; Tahyudin and Nambo, 2019; Kuo et al., 2019; Ledmi et al., 2021; Moslehi et al., 2020)
Stock Price |  Opt. |  (Mata et al., 2002; Martínez-Ballesteros et al., 2011; Martín et al., 2014b, a; Martínez-Ballesteros et al., 2016; Martín et al., 2016; Alatas et al., 2008; Almasi and Abadeh, 2015; Can and Alatas, 2017)
Stulong |  Opt. |  (Martín et al., 2014b, a, 2016; Altay and Alatas, 2021; Almasi and Abadeh, 2015; Salleb-Aouissi et al., 2007)
Vineyard |  Opt. |  (Mata et al., 2002; Martínez-Ballesteros et al., 2011, 2016; Altay and Alatas, 2021)
Segment |  Opt. |  (Martín et al., 2014a, 2016; Luna et al., 2014)
Bodyfat |  Opt. |  (Mata et al., 2002; Martínez-Ballesteros et al., 2011; Minaei-Bidgoli et al., 2013; Martínez-Ballesteros et al., 2016; Moslehi and Haeri, 2020; Altay and Alatas, 2021; Alatas et al., 2008; Altay and Alatas, 2022; Alatas and Akin, 2008, 2009; Beiranvand et al., 2014; Kahvazadeh and Abadeh, 2015; Heraguemi et al., 2018; Kuo et al., 2019; Tahyudin and Nambo, 2019; Ledmi et al., 2021; Moslehi et al., 2020)
Ailerons |  Opt. |  (Altay and Alatas, 2021; Martínez-Ballesteros et al., 2016; Ledmi et al., 2021)
Elevators |  Opt. |  (Ledmi et al., 2021; Altay and Alatas, 2021; Martínez-Ballesteros et al., 2016)
Longley |  Opt. |  (Altay and Alatas, 2021; Martínez-Ballesteros et al., 2016, 2011)
Sleep |  Opt. |  (Mata et al., 2002; Alataş and Akin, 2006; Martínez-Ballesteros et al., 2011, 2016; Alatas et al., 2008; Alatas and Akin, 2008; Moslehi et al., 2011)
UCI |  Iris |  Opt. Disc. etc. |  (Salleb-Aouissi et al., 2007; Song and Ge, 2013; Ling et al., 2014; Li et al., 1999; Hu et al., 2022; Jaramillo et al., 2021)
Weather Ankara |  Opt. |  (Martínez-Ballesteros et al., 2016; Ledmi et al., 2021; Luna et al., 2014)
Thyroid |  Opt. Etc. |  (Ke et al., 2008; Can and Alatas, 2017; Martín et al., 2016)
WBC |  Disc. |  (Miller and Yang, 1997; Li et al., 1999; Ling et al., 2014)
WDBC |  Opt. Etc. |  (Luna et al., 2014; Martín et al., 2016; Zheng et al., 2014)
Abalone |  Disc. Stat. Etc. |  (Webb, 2001; Song and Ge, 2013; Jaramillo et al., 2021)
Synthetic |  Opt. Stat. Etc. |  (Mata et al., 2001, 2002; Alataş and Akin, 2006; Salleb-Aouissi et al., 2007; Martínez-Ballesteros et al., 2010; Álvarez and Vázquez, 2012; Alatas et al., 2008; Taboada et al., 2008; Srikant and Agrawal, 1996; Lent et al., 1997; Wang et al., 1998; Kuok et al., 1998; Zhang, 1999; Lud and Widmer, 2000; Fukuda et al., 1999; Lee, 2001; Brin et al., 1999; Rastogi and Shim, 2002; Lian et al., 2005; Ke et al., 2008; Jaramillo et al., 2021; Kang et al., 2009; Alatas and Akin, 2008, 2009; Moslehi et al., 2011)
Figure 4. Datasets Used by Discretization Method. Table 15. Data-sets Used by Discretization Method for NARM Algorithms Datasets |  References
---|---
Wisconsin Breast Cancer Data |  (Miller and Yang, 1997; Li et al., 1999; Ling et al., 2014)
Wisconsin Prognostic Breast Cancer |  (Zheng et al., 2014)
Wisconsin Diagnostic Breast Cancer |  (Zheng et al., 2014)
Glass Identification |  (Li et al., 1999)
Heart Disease |  (Li et al., 1999)
Shuttle |  (Yang and Feng, 2010; Guo et al., 2008)
The Car |  (Guo et al., 2008)
Seed |  (Ling et al., 2014)
Pima Indian diabities |  (Zheng et al., 2014; Song and Ge, 2013)
Wine |  (Ling et al., 2014)
Auto MPG |  (Song and Ge, 2013)
Abalone |  (Song and Ge, 2013)
Census-lncome |  (Chan and Au, 1997a)
Iris |  (Song and Ge, 2013; Ling et al., 2014; Li et al., 1999)
Zoo |  (Chan and Au, 1997a)
Synthetic |  (Srikant and Agrawal, 1996; Lent et al., 1997; Wang et al., 1998; Kuok et al., 1998; Zhang, 1999; Lud and Widmer, 2000; Fukuda et al., 1999; Lee, 2001; Brin et al., 1999; Rastogi and Shim, 2002; Lian et al., 2005)
Other real-world datasets |  (Büchter and Wirth, 1998; Chan and Au, 1997b; Hong et al., 1999; Lud and Widmer, 2000; Gyenesei, 2001; Mohamadlou et al., 2009; Dong and Pi, 2014; Medjadba et al., 2020; Fan et al., 2019)

###  4.7. RQ7 What are the challenges and potential future perspectives for
the area of NARM?

To address this research question, a manual identification of the existing
research challenges in NARM was conducted. Additionally, the focus was placed
on identifying future directions for NARM research.

####  4.7.1. Research Challenges

After a comprehensive analysis of various NARM methods in both static and
dynamic settings, we have identified several issues that NARM needs to
address.

  * •

_Handling Skewed Data:_ NARM faces challenges when dealing with skewed data,
where the data distribution is uneven. Finding associations between numerical
variables in such datasets can be difficult and lead to biased results, as
well as a high number of irrelevant rules. Moreover, skewed data can have a
negative impact on the accuracy and reliability of the analysis, potentially
resulting in biased conclusions. Calculation of support and confidence
measures can be particularly affected, leading to inaccurate values and
erroneous assessments of rule strength. Furthermore, processing skewed data
can also impact the speed and efficiency of the algorithms, as they may need
to handle a large number of extraneous rules.

  * •

_Handling a Large Number of Rules:_ The main objective of mining numerical
association rules is to discover relationships between numerical variables in
large datasets. However, this often results in a vast number of association
rules, which can make the process computationally expensive, time-consuming,
and difficult to sift through to identify the most relevant or interesting
rules. To address this challenge, several techniques have been developed to
simplify the process and make it more manageable. Some of these techniques
include data sampling, the use of efficient algorithms, parallel and
distributed computing, dimensionality reduction, and pruning methods. By
implementing these techniques, it becomes easier to extract useful association
rules from large datasets, reduce the size of the dataset, simplify the mining
process, and speed up computations.

  * •

_Quality of Association Rules:_ Extracting high-quality rules is also a
challenge in NARM due to the potential for redundancy, irrelevance, and
conflicts in the rules. The large and complex nature of the datasets used in
NARM can lead to a large number of rules, making it difficult to identify the
most relevant ones. Additionally, skewed data can impact the reliability of
support and confidence measures, further affecting the quality of the rules.
The rules generated by NARM algorithms may also be challenging to interpret
and understand. To address these issues, data pre-processing, the use of
alternative metrics, the selection of appropriate algorithms, and the
application of ensemble methods can be helpful in improving the quality of
association rules.

  * •

_Complex Relationship:_ Numerical data often contain intricate relationships,
such as non-linear or multi-dimensional relationships, which can be difficult
to represent and analyze using traditional ARM algorithms. This may result in
inaccurate or incomplete rules, which can impact the reliability and accuracy
of the analysis. To address this challenge, advanced algorithms such as
decision trees, artificial neural networks, or support vector machines can be
utilized in NARM. Ensemble approaches like gradient boosting or random forests
can also be helpful in addressing complex relationships by combining the
output of multiple algorithms to produce more accurate results. However, these
techniques may increase computational complexity and require more data and
computing resources to be effective.

  * •

_Handling Outliers:_ Outliers are extreme values that differ significantly
from the majority of values in the dataset and can impact the accuracy and
reliability of the results of ARM. Outliers may indicate genuine data
variances, or they could result from measurement errors or data input issues.
Several methods can address this problem, including outlier detection, data
cleaning, data transformation, and robust algorithms. These methods can help
remove or mitigate the effect of outliers, ensuring that the mining process
yields more accurate and reliable results.

####  4.7.2. Future directions

  * •

_Handling Big Data:_ Despite conducting a thorough SLR, we were unable to
identify any studies that focus on retrieving numerical association rules from
big data. However, the rise of big data will undoubtedly have a significant
impact on the future of NARM. Developing more efficient algorithms that can
handle vast amounts of numerical data will be essential as big data continues
to become increasingly common. This will likely lead to the development of new
algorithms specifically designed for big data that are optimized for
scalability, speed, and accuracy. Additionally, advanced data cleanings and
preprocessing techniques, such as outlier detection, imputation of missing
values, and feature selection, will become increasingly important to ensure
the quality of results.

  * •

_Explainable AI:_ Improving the interpretability and explainability of NARM
results is a critical research direction. Explainable AI (Barredo Arrieta et
al., 2020; au2 et al., 2023) can enhance transparency and comprehension, which
is essential for non-experts to validate the findings and ensure their
alignment with the intended objectives. By revealing the underlying reasoning
behind the results, Explainable AI can assist users in making better decisions
and recognizing any inherent biases or limitations in the outcomes. Therefore,
developing NARM techniques that provide transparent and comprehensible results
is a vital area of research.

  * •

_Hybrid Approach:_ A promising future direction in NARM is to leverage the
strengths of various methods and techniques through hybrid approaches. Some
studies, such as (Moslehi et al., 2020; Altay and Alatas, 2022, 2021; Zhang,
1999; Mohamadlou et al., 2009; Wang et al., 2015; Kianmehr et al., 2010), have
attempted to combine different approaches to improve the results of NARM.
Combining NARM with deep learning, integrating rule-based and distance-based
approaches, and combining unsupervised and supervised learning are all hybrid
approaches that show potential in this field. By integrating these techniques,
the limitations of individual methods can be addressed, resulting in a more
accurate and thorough analysis of the relationships between variables in
numerical data. Hybrid approaches can lead to valuable insights and more
reliable results.

  * •

_Handling Streaming Data:_ To keep up with the increasing demand for real-time
analysis, developing NARM algorithms that can handle streaming data and update
association rules in near real-time is crucial. In applications where timely
and accurate decisions are critical, streaming data enables real-time analysis
of numerical data, which allows organizations to make informed decisions based
on up-to-date information. The ability to analyze a larger volume of numerical
data in real time will lead to more comprehensive and accurate results.
Moreover, streaming data enables dynamic updates to the results of NARM as new
data becomes available, providing a more accurate and comprehensive view over
time. Therefore, developing NARM algorithms that can handle streaming data is
an important future direction.

  * •

_Incorporating Machine Learning Techniques:_ The integration of machine
learning techniques, such as deep learning, into NARM, has the potential to
revolutionize the field. With the ability to automatically detect patterns and
relationships in the data, which may not be immediately apparent to human
analysts, machine learning algorithms can significantly enhance the accuracy
of the results. Moreover, this approach can reduce the time and effort
required to identify such patterns in the data. The utilization of machine
learning can also expand the scope of NARM applications across various
industries, as these algorithms can handle complex data more efficiently,
including high-dimensional data or data with non-linear relationships.

  * •

_Privacy and Security:_ The importance of privacy and security in NARM is
increasing, and it is imperative to protect and use data ethically. However,
the existing studies in this SLR did not address these issues. To ensure data
protection, sensitive information can be removed or masked using anonymization
techniques while preserving the necessary data for ARM. Furthermore, to reduce
the risk of unauthorized access, the data can be partitioned into smaller
subsets, and access control methods can be developed to control who has access
to the data and the association rules generated from it. Incorporating these
privacy and security measures will safeguard the data and ensure its ethical
use.

###  4.8. RQ8 How to automate discretization of numerical attributes for NARM
in a useful (natural) manner?

Developing novel methods and techniques for NARM is a continuous area of
research, and the discretization method serves as the foundation for NARM
(Srikant and Agrawal, 1996). However, selecting the best partitions for
discretizing complex real-world datasets still lacks a benchmark method. None
of the discretization methods that we figured out with the review, see Sects.
4.1.1 in 4.2.1, explicitly addresses human perception of partitions.
Therefore, we proposed a novel discretization technique in our research
(Kaushik et al., 2021a), which utilizes two measures for order-preserving
partitioning of numerical factors: the _Least Squared Ordinate-Directed Impact
Measure_ (LSQM) and the _Least Absolute-Difference Ordinate-Directed Impact
Measure_ (LADM).

These proposed measures offer a straightforward method for finding partitions
of numerical attributes that reflect best the impact of one independent
numerical attribute on a dependent numerical attribute. We thoroughly
experimented with these measures and compared the outcomes with human
perceptions of partitioning a numerical attribute (Kaushik et al., 2022b). To
develop an automated measure for discretizing numerical attributes,
understanding perceptual conception is crucial. To achieve this, we
investigated the impact of data points’ features on human perception when
partitioning numerical attributes (Kaushik et al., 2022a). These efforts have
contributed to the development of more accurate and efficient methods for
NARM.

##  5\. Threats to validity

This section outlines potential threats to the validity of this SLR that might
bias the outcomes of our in-depth investigation. The first threat pertains to
defining the search string. We make no claims regarding the perfection of the
search string used in the process. While we included all relevant search terms
related to NARM, it is possible that the search terms may not have captured
all relevant NARM-related work. To mitigate this risk, we included synonyms
for “numerical association rule mining” and abbreviations such as “NARM” and
“QARM” in the search term. The second threat pertains to the selection of
digital libraries to search for articles. Although we searched five digital
libraries for computer science, it is possible that additional sources may
have produced different outcomes. To minimize bias, we manually searched
Google Scholar and also looked through the list of references for the selected
primary studies to identify significant publications. We are confident that
the majority of published research on NARM is covered in this study. The third
threat is the inclusion and exclusion of articles. To determine whether a
paper should be included or excluded, we first reviewed the title, abstract,
and keywords according to the inclusion and exclusion criteria. Then, we
manually checked for references to ensure we did not miss any relevant papers.
Additionally, we evaluated the selected studies using a quality assessment
procedure. The fourth threat is about the time frame. Since the search process
was conducted in early 2022, only articles published between 1996 and the
beginning of 2022 were included. It is possible that we may have missed some
articles published after the specified time frame. The final threat concerns
article selection. The authors of this article may have been biased in their
choice and categorization of publications that were included. Two authors
chose articles based on their personal experiences. Although the final
selection was made by a single author, all studies were verified by other
authors to minimize bias.

##  6\. Discussion and Finding

In this SLR, we analyzed a total of $68$ studies on NARM published between
1996 and 2022. Our analysis revealed several significant findings and trends.
Figure 5 illustrates the distribution of selected articles by publication
year, with a notable concentration in 2014 and strong NARM trends in 2019 and
2021.

Figure 5. Distribution of Articles by Publication Year.

(a) Distribution of the Articles by Method.

(b) Proportions of the Articles.

Figure 6. Distribution of the Primary Study.

(a) Articles Published in Journals.

(b) Articles Published in Conferences.

(c) Total Articles Published in Different Sources.

Figure 7. Publication Source Distribution: 1996-2022. Table 16. Type of Publications in the Area of NARM Publication Source |  Type of Publication |  Number of Articles
---|---|---
IEEE |  Journal |  3
Springer |  Journal |  11
ACM |  Journal |  1
ScienceDirect |  Journal |  14
Other |  Journal |  12
IEEE |  Conference |  11
Springer |  Conference |  6
ACM |  Conference |  9
Other |  Conference |  1
Table 17. Advantages and Limitations of NARM Methods NARM Method |  Advantages |  Limitations
---|---|---
The Discretization method |  Easier to interpret. Efficient and scalable to process huge datasets. Able to deal with continuous variables. Highly flexible. |  Requirement of a user-specified threshold. Information loss. Unable to deal with high dimensional data. Discretization bias can lead to inaccurate or unreliable results. Membership functions should be known in advance.
The Optimization method |  No need to determine the minimum support and minimum confidence. No need for a prior discretization step. High scalability. |  Higher computational cost. Low search capability in the local area. Stuck in local minima. Convergence issues.
The Statistical method |  Measuring significance to identify meaningful association rules. Can handle noise and missing data. Provide quantifiable results. |  Lack of scalability. Not able to detect complex relationships in data. Not suitable for handling high-dimensional data. Not well-suited for mining rules in numerical data.
Figure 8. Visual Exploration and Illustration of NARM Methods and Algorithms:
A Comprehensive Overview.

The distribution of primary studies by the method is presented in Figure 6.
The majority of articles focus on the discretization method, followed by the
evolution-based optimization method (Figure 6(a)). Statistical and other
techniques make up a smaller portion. We further detail the partition of
articles by the method as a percentage in Figure 6(b). Table 16 provides the
number of articles published in different journals and conferences. Overall,
$60\%$ of the selected articles were published in journals and $40\%$ in
conferences. Figure 7 illustrates the percentage of articles published in
journals and conferences. ScienceDirect published $34\%$ journal articles
(Figure 7(a)) however, IEEE published $41\%$ conference articles (Figure
7(b)), which is the highest number and Springer published $25\%$ overall
articles (Figure 7(c)). We identified several methods for solving NARM,
including the discretization method, optimization methods using evolutionary
and bio-inspired approaches, the statistical method, and other methods. The
discretization method was the most widely used, accounting for $39\%$ of the
total articles (Figure 6(b)). Evolution-based and SI-based optimization
methods covered $32\%$ and $20\%$ of the total articles, respectively (Figure
6(b)). The optimization and discretization methods had a greater impact
compared to the statistical and other methods. Each method also employs
various approaches to address NARM problems. For example, the discretization
method includes the clustering approach, which encompasses density-based and
grid-based techniques. The partition approach, which involves converting
continuous numerical data into discrete values by grouping them into intervals
or bins, was found to be simple and easy to implement. However, the choice of
interval or bin size can affect result accuracy, and it may not be suitable
for datasets with a large number of variables. The optimization method
encompasses a range of approaches, such as genetic algorithms, grammar-guided
genetic programming, differential evolution, particle swarm optimization,
gravity-based algorithms, swarm-based algorithms, Cauchy distribution, and
hybrid-based methods. The statistical method is limited and utilizes various
distribution scales, including mean, median, variance, and standard deviation.
Some studies did not fit into any specific method and were categorized as
miscellaneous other methods based on the information-theoretic approach,
cognitive computing, and variable mesh.

Many algorithms (Lent et al., 1997; Lud and Widmer, 2000; Hong et al., 1999;
Zhang, 1999; Zheng et al., 2014; Srikant and Agrawal, 1996; Wang et al., 2015)
based on the discretization method use apriori algorithm for generating
association rules. However, the evolution and SI-based algorithms do not use
the apriori algorithm. Additionally, certain algorithms under the
discretization method have employed new measures, including density measure
(Lian et al., 2005; Guo et al., 2008), R-measure (Zhang, 1999), Certainty
Factor (Zheng et al., 2014), and adjusted difference measure (Chan and Au,
1997a). Figure 8 demonstrates the visual presentation of NARM methods and
their algorithms. In Table 17, we have summarized the advantages and
limitations of each method.

Our analysis found that most studies based on the discretization method used
synthetic and real-world data to evaluate the effectiveness of NARM
algorithms. However, evolutionary and SI-based algorithms mostly used common
datasets, such as _Quake_ , _Basketball_ , _Bolt_ , _Bodyfat_. Furthermore,
the _Iris_ dataset was the only one commonly used by discretization,
optimization, and statistical methods-based algorithms. It is crucial to note
that the choice of the dataset may impact the performance of the methods, and
further studies are needed to evaluate the algorithms’ practical applicability
on real-world datasets.

In our extensive review of the literature on NARM, we analyzed various metrics
used to evaluate the performance and effectiveness of different algorithms and
models. Our study focused on important metrics such as generated number of
rules, run time, and value of support and confidence. Support measures the
frequency of a specific item set or rules in the dataset, frequently used in
conjunction with confidence, which quantifies how likely a certain outcome is
given an antecedent. However, it is crucial to carefully interpret and
evaluate the reliability and validity of the metrics used, as they can lead to
spurious or irrelevant associations if not used properly. Our SLR sheds light
on the most commonly used metrics in NARM, including those used by multi-
objective algorithms.

Multi-objective NARM algorithms consider different objectives simultaneously
to generate a set of Pareto-optimal solutions that balance competing
objectives. The choice of objective for multi-objective NARM algorithms
depends on the research question and data characteristics. Some common
objectives include maximizing support or confidence while minimizing the
number of rules generated.

As a rapidly evolving research area, NARM presents numerous potential future
directions for research. These include exploring new scalable optimization
algorithms, addressing Big Data challenges, incorporating explainable AI into
the mining process, integrating machine learning techniques, addressing
security concerns, and using hybrid approaches. By pursuing these directions,
researchers can advance the state of the art in NARM and develop more
effective and practical solutions for real-world applications.

##  7\. Conclusion

This article addressed a significant research gap in the field of NARM and
provided readers with a comprehensive understanding of the state-of-the-art
methodologies and developments in the domain. Moreover, this study serves as a
foundation for future research and offers comprehensive insights for
researchers working on NARM-related problems. To achieve this, a comprehensive
SLR is conducted based on the guidelines set forth by Kitchenham and Charter.
We conducted a detailed examination of a wide range of methods, algorithms,
metrics, and datasets sourced from 1,140 scholarly articles spanning the
period from the introduction of NARM in 1996 to 2022. Eventually, through a
rigorous selection process, including several inclusion, exclusion and quality
assessment criteria, 68 articles were selected for this SLR. By providing an
exhaustive understanding of the existing NARM methods, highlighting their
strengths and limitations, as well as identifying research challenges and
future directions, we aim to stimulate innovative thinking and encourage the
exploration of novel approaches in NARM. These perspectives include exploring
new scalable optimization algorithms, analyzing NARM methods with big data,
incorporating explainable AI into the mining process, incorporating machine
learning techniques, addressing security concerns, and using hybrid
approaches. Subsequently, based on the finding of this SLR, a novel
discretization measure is presented to aid in NARM that explicitly addresses
the human perception of partitions. The ultimate goal of this review is to
inspire and guide researchers in developing more effective and practical
solutions for real-world NARM applications.

###### Acknowledgements.

This work has been partially conducted in the project “ICT programme” which
was supported by the European Union through the European Social Fund.

## Declarations

Conflict of interest Authors declare that they have no conflict of interest.

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