Revision of paper

Author: firefly-cpp

paper/paper.md

@@ -1,5 +1,5 @@
 ---
-title: 'NiaARM: A minimalistic framework for numerical association rule mining'
+title: 'NiaARM: A minimalistic framework for Numerical Association Rule Mining'
 tags:
   - Python
   - association rule mining
@@ -32,41 +32,40 @@ Association Rule Mining (ARM) is a data mining method intended for discovering r
 transaction databases in the form of implications [@agrawal_fast_1994; @fister_jr_brief_2020]. Traditional
 approaches, such as the Apriori algorithm [@agrawal_fast_1994] or ECLAT [@zaki_scalable_2000],
 require the attributes in the database to be discretized. This can result in the incorporation of noise into data,
-and potentially obtained associations may not fully reveal the story [@varol2020performance]. On the contrary,
-Numerical association rule mining (NARM) is an extension of ARM that allows handling numerical attributes without
+and potentially the obtained associations may not reveal the story fully [@varol2020performance]. On the contrary,
+Numerical Association Rule Mining (NARM) is an extension of ARM that allows handling numerical attributes without
 discretization [@fister_jr_improved_2021; @kaushik2020potential]. Thus, an algorithm can operate directly, not only with
 categorical but also with numerical attributes concurrently. Interestingly, most NARM algorithms are based on
 stochastic population-based nature-inspired algorithms, which proved to be very efficient in searching for association
 rules [@alatas2008modenar; @kaushik2021systematic].
 
-The NiaARM framework is an implementation of the ARM-DE algorithm [@fister_differential_2018; @fister_jr_improved_2021], where
-numeric association rule mining is modeled as a single objective, continuous optimization problem, where the fitness is a
-weighted sum of the support and confidence of the built rule. The approach is extended by allowing the use of any optimization
-algorithm from the related NiaPy framework [@vrbancic_niapy_2018], as well as having the option to select various interest
+The NiaARM framework is an extended implementation of the ARM-DE algorithm [@fister_differential_2018; @fister_jr_improved_2021], where
+Numerical Association Rule Mining is modeled as a single objective, continuous optimization problem, where the fitness is a weighted sum of the support and confidence of the built rule. The approach is extended by allowing the use of any optimization
+algorithm from the related NiaPy framework [@vrbancic_niapy_2018] and having the option to select various interest
 measures and their corresponding weights for the fitness function.
 
 The flow of the NiaARM framework is shown in \autoref{fig:NiaARM}. Users have the option to construct a dataset either from a
-CSV file or a pandas DataFrame. The dataset is then used to build the optimization problem, along with user selected interest
+CSV file or a pandas Dataframe. The dataset is then used to build the optimization problem, along with user selected interest
 measures to be used in the computation of the fitness function. Then the optimization problem can be solved using any algorithm
-in the NiaPy library to mine association rules from the dataset. The rules can be exported to a CSV file, statistically
-analysed or visualized using the visualization methods implemented in the framework, such as the hill slopes method
+in the NiaPy library to mine association rules from the dataset. The rules can be exported to a CSV file,
+analyzed statistically, or visualized using the visualization methods implemented in the framework, such as the hill slopes method
 [@fister_visualization_2020]. A simple command-line interface for mining rules is also provided.
 
 ![NiaARM flow.\label{fig:NiaARM}](NiaARM1.png)
 
 # Statement of need
 
-Numerical association rule mining plays a vital role in the data revolution era [@telikani_survey_2020]. Several research
+Numerical Association Rule Mining plays a vital role in the data revolution era [@telikani_survey_2020]. Several research
 papers that present NARM methods exist, but universal software where all primary tasks of NARM, i.e., preprocessing, searching
-for association rules, and visualization, are lacking. The NiaARM framework provides users with methods that allow them to
+for association rules, and visualization, is lacking. The NiaARM framework provides users with methods that allow them to
 preprocess their data, implement several interest measures, and powerful visualization techniques. In a nutshell, the benefits
 of the NiaARM framework are:
 
-1. Simple way to mine association rules on numerical, categorical, or mixed attribute-type datasets.
+1. A simple way to mine association rules on numerical, categorical, or mixed attribute-type datasets.
 
 2. Combined with the NiaPy library, it allows testing out the proposed approach using arbitrary nature-inspired algorithms.
 
-3. A vast collection of implemented popular interest measures to measure mined rules' quality.
+3. A vast collection of implemented popular interest measures to measure the mined rules' quality.
 
 4. Powerful visualization methods.