Update README.md

Author: Fuminides

README.md

@@ -6,6 +6,7 @@ Some of the tools available in this library include:
 - Support for approximate reasoning using fuzzy association rules, for both classification and regression problems. This includes rule base optimization using genetic algorithms and rule visualization.
 - Precomputed and optimized fuzzy variables and their correspondent linguistic variables (i.e low, medium, high).
 - Support for various kinds of fuzzy sets, including classic fuzzy sets, IV-fuzzy sets and General Type 2 fuzzy sets.
+- Rule mining using support, confidence and lift measures. Customizable genetic optimization of the rule bases parameters.
 
 ## Main Characteristics
 
@@ -15,22 +16,22 @@ ex-Fuzzy is designed to be easy to use. Linguistic variables can be precomputed
 
 ### Reusable code
 
-Code is designed so that some parts can be easily extendable, so that some use cases, like research, can be also supported. The rule base optimization is done using a Genetic Algorithm, but almost any other pymoo search algorithm will do. Fuzzy sets can be extended with ease, just as the kind of partitions, membership functions, etc.
+Code is designed so that some parts can be easily extendable so that some use cases, like research, can be also supported. The rule base optimization is done using a Genetic Algorithm, but almost any other pymoo search algorithm will do. Fuzzy sets can be extended with ease, just as the kind of partitions, membership functions, etc.
 
 ### Sci-py like interface
 
 ex-Fuzzy is built taking into account the actual machine-learing frameworks used in Python. Training amd sing a rule base classifier works exactly as sci-kit learn classifier. Parameters such as the number of rules or antecedents are also built 
 
-### Visualize the results
+### Visualization
 
 Use plots to visualize any kind of fuzzy sets, and use graphs to visualize rules or print them on screen.
 
 
 <p align="center">
   <img src="https://user-images.githubusercontent.com/12574757/210235257-17b22ede-762b-406c-880a-497e06964f17.png" width="350" title="Fuzzy graph">
   <img src="https://user-images.githubusercontent.com/12574757/210235264-be98fff9-d1b6-4f3b-8b93-b11e0466a48c.png" width="350" title="Type 1 example">
-  <img src="https://private-user-images.githubusercontent.com/12574757/310877934-89b7184e-5dcc-445f-8b5f-7d4e9388c56f.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.7SKQ0PRxT91Q-t6BN4KDk-l6WNjDtTjfhOgq8ih_cvo" width="350" title="Type 2 example">
-  <img src="https://private-user-images.githubusercontent.com/12574757/310877940-cf4453fe-6f82-4f49-b418-c774729022f7.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.D7dvwn_gkW5SVWBhOkNywlGgUiSzl-HABPcBte1j3gE" width="350" title="General Type 2 example">
+  <img src="https://github.com/Fuminides/exFuzzy/assets/12574757/b37e2f4e-0e19-4d4b-a504-b121e41c9399" width="350" title="Type 2 example">
+  <img src="https://github.com/Fuminides/exFuzzy/assets/12574757/8e3c036f-2ab7-4281-8ef8-b8891fbf354a" width="350" title="General Type 2 example">
 
 </p>
 
@@ -48,31 +49,6 @@ You can install ex-Fuzzy using pip, from the PyPi repository, with the following
 
 `pip install ex-fuzzy`
 
-## Home Page
-
-Find all the information about ex-fuzzy in the github repository page:
-
-[https://github.com/Fuminides/ex-fuzzy](https://github.com/Fuminides/ex-fuzzy)
-
-## Documentation
-
-The documentation for ex-Fuzzy is available in: 
-
-[https://fuminides.github.io/ex-fuzzy/](https://fuminides.github.io/ex-fuzzy/)
-
-## Try the demos!
-
-The Demos folder contains a series of demos to try different features of the ex-fuzzy library. These are presented in two different formats: jupyter notebooks and python modules, which are stored under the demos_module folder. You dont need to install the library to execute them.
-
-The list of demos is the following:
-
-1. iris_demo: shows a simple classification example. It shows how to train a classifier, how to save checkpoints, how to show the rules in latex tabular format and to save them into a text file.
-2. iris_demo_custom_loss: a classification example where the predefined loss is changed by other function.
-3. iris_demo_persistence: a classification example where the rules are saved into a file and then imported for another classifier.
-4. precandidate_rules_demo: a classification example where we first fit a fuzzy classifier as usual, and then, we look for the optimal subset of those rules.
-5. regression_demo: an example of a regression problem using inerval-type 2 fuzzy sets.
-6. occupancy_demo_temporal: an example of the use of temporal fuzzy sets.
-
 
 ## Contributors
 Javier Fumanal Idocin, Javier Andreu-Perez