rename interest measures to interestingness measures

Author: zStupan

README.md

@@ -218,11 +218,10 @@ problem.rules.sort()
 problem.rules.to_csv('output.csv')
 ```
 
-#### Interest measures
+#### Interestingness measures
 
-The framework implements several popular interest measures, which can be used to compute the fitness function value of rules
-and for assessing the quality of the mined rules. A full list of the implemented interest measures along with their descriptions
-and equations can be found [here](interest_measures.md).
+The framework implements several popular interestingness measures, which can be used to compute the fitness function value of rules
+and for assessing the quality of the mined rules. A full list of the implemented interestingness measures along with their definitions can be found [here](interestingness_measures.md).
 
 ### Visualization
 

docs/getting_started.rst

@@ -108,7 +108,7 @@ objective, unconstrained maximization problem (more details on this approach can
 `here <https://link.springer.com/chapter/10.1007/978-3-030-68154-8_19>`__ and
 `here <http://www.iztok-jr-fister.eu/static/publications/231.pdf>`__).
 To summarize, for each solution vector a :class:`~niaarm.rule.Rule` is built,
-and it's fitness is computed as a weighted sum of selected interest measures (metrics).
+and it's fitness is computed as a weighted sum of selected interestingness measures (metrics).
 The rule is then appended to a list of rules, which can be accessed through the NiaARM class.
 
 The :class:`~niaarm.niaarm.NiaARM` class takes the dataset's
@@ -317,10 +317,10 @@ added to the :mod:`niaarm.mine` module.
     Run time: 13.37s
     Rules exported to output.csv
 
-Interest Measures
+Interestingness measures
 -----------------
 
-The framework currently implements the following interest measures (metrics):
+The framework currently implements the following interestingness measures (metrics):
 
 - Support
 - Confidence
@@ -336,7 +336,7 @@ The framework currently implements the following interest measures (metrics):
 - Yule's Q [#fn]_
 - Zhang's Metric [#fn]_
 
-More information about these interest measures can be found in the API reference
+More information about these interestingness measures can be found in the API reference
 of the :class:`~niaarm.rule.Rule` class.
 
 .. rubric:: Footnotes

interest_measures.md interestingness_measures.mdinterest_measures.md renamed to interestingness_measures.md

@@ -1,4 +1,4 @@
-# Interest Measures
+# Interestingness measures
 
 ## Support
 

paper/paper.md

@@ -53,19 +53,19 @@ analyzed statistically, or visualized using the visualization methods implemente
 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, 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
+preprocess their data, implement several interestingness measures, and powerful visualization techniques. In a nutshell, the benefits
 of the NiaARM framework are:
 
 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 the mined rules' quality.
+3. A vast collection of implemented popular interestingness measures to measure the mined rules' quality.
 
 4. Powerful visualization methods.
 
 5. A simple command-line interface for easier handling with the proposed tool.
 
-To the authors' knowledge, NiaARM is one of only three publicly available software solutions that implement any form of numerical association rule mining, the other two being KEEL [@alcala2009keel] and uARMSolver [@fister_uarmsolver_2020]. KEEL is a software tool used to assess evolutionary algorithms for machine learning problems of various kinds such as regression, classification, unsupervised learning, etc. It's a GUI application written in Java primarily intended for research and educational purposes. Although its scope is much wider it also includes some popular algorithms for numerical association rule mining including GAR, GENAR and MODENAR. The uARMSolver framework, written in C++, also implements the ARM-DE algorithm. Comparatively, NiaARM offers better ease of use, the ability to use arbitrary nature-inspired algorithms from the NiaPy framework (uARMSolver only implements DE and PSO), and the ability to optimize using more interest measures.
+To the authors' knowledge, NiaARM is one of only three publicly available software solutions that implement any form of numerical association rule mining, the other two being KEEL [@alcala2009keel] and uARMSolver [@fister_uarmsolver_2020]. KEEL is a software tool used to assess evolutionary algorithms for machine learning problems of various kinds such as regression, classification, unsupervised learning, etc. It's a GUI application written in Java primarily intended for research and educational purposes. Although its scope is much wider it also includes some popular algorithms for numerical association rule mining including GAR, GENAR and MODENAR. The uARMSolver framework, written in C++, also implements the ARM-DE algorithm. Comparatively, NiaARM offers better ease of use, the ability to use arbitrary nature-inspired algorithms from the NiaPy framework (uARMSolver only implements DE and PSO), and the ability to optimize using more interestingness measures.
 
 # References

src/niaarm/rule.py

@@ -13,7 +13,7 @@ class Rule:
         transactions (pandas.DataFrame | None): Transactional database.
 
     Attributes:
-        cls.metrics (tuple[str]): List of all available interest measures.
+        cls.metrics (tuple[str]): List of all available interestingness measures.
         support: Support is defined on an itemset as the proportion of transactions
         that contain the attribute :math:`X`.