Refactor README to enhance clarity and structure.

Simplified and restructured content for better readability and usability. Updated badges, added concise feature descriptions, and improved example code formatting. Introduced a detailed table of contents and clarified usage instructions.

Author: jacksonpradolima

README.md

@@ -1,12 +1,6 @@
-[!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/pradolima)
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-
-
-# GSP-Py
-A **Python implementation** of the Generalized Sequence Pattern (GSP) algorithm for mining sequential patterns in datasets. GSP is a powerful algorithm for discovering sequences of events or items that are frequently observed, making it suitable for a wide range of domains such as market basket analysis, web usage mining, and bioinformatics.
-
 [![PyPI License](https://img.shields.io/pypi/l/gsppy.svg?style=flat-square)]()
-![](https://img.shields.io/badge/python-3.11.4+-blue.svg)
+[![PyPI Downloads](https://img.shields.io/pypi/dm/gsppy.svg?style=flat-square)](https://pypi.org/project/gsppy/)
+![](https://img.shields.io/badge/python-3.11+-blue.svg)
 [![DOI](https://zenodo.org/badge/108451832.svg)](https://zenodo.org/badge/latestdoi/108451832)
 
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@@ -15,37 +9,50 @@ A **Python implementation** of the Generalized Sequence Pattern (GSP) algorithm
 [![Maintainability Rating](https://sonarcloud.io/api/project_badges/measure?project=jacksonpradolima_gsp-py&metric=sqale_rating)](https://sonarcloud.io/summary/new_code?id=jacksonpradolima_gsp-py)
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+# GSP-Py
+
+**GSP-Py**: A Python-powered library to mine sequential patterns in large datasets, based on the robust **Generalized
+Sequence Pattern (GSP)** algorithm. Ideal for market basket analysis, temporal mining, and user journey discovery.
+
 ---
 
 ## 📚 Table of Contents
-- [What is GSP?](#what-is-gsp)
-- [Requirements](#requirements)
-- [Installation](#installation)
-- [Developer Installation](#developer-installation)
-- [Usage](#usage)
-- [Planned Features](#planned-features)
-- [Contributing](#contributing)
-- [License](#license)
-- [Citation](#citation)
+
+1. [🔍 What is GSP?](#what-is-gsp)
+2. [🔧 Requirements](#requirements)
+3. [🚀 Installation](#installation)
+    - [❖ Clone Repository](#option-1-clone-the-repository)
+    - [❖ Install via PyPI](#option-2-install-via-pip)
+4. [🛠️ Developer Installation](#developer-installation)
+5. [💡 Usage](#usage)
+    - [✅ Example: Analyzing Sales Data](#example-analyzing-sales-data)
+    - [📊 Explanation: Support and Results](#explanation-support-and-results)
+6. [🌟 Planned Features](#planned-features)
+7. [🤝 Contributing](#contributing)
+8. [📝 License](#license)
+9. [📖 Citation](#citation)
 
 ---
 
 ## 🔍 What is GSP?
 
-The **Generalized Sequential Pattern (GSP)** algorithm is a sequential pattern mining technique based on **Apriori principles**. Using support thresholds, GSP identifies frequent sequences of items in transaction datasets.
+The **Generalized Sequential Pattern (GSP)** algorithm is a sequential pattern mining technique based on **Apriori
+principles**. Using support thresholds, GSP identifies frequent sequences of items in transaction datasets.
 
 ### Key Features:
+
 - **Support-based pruning**: Only retains sequences that meet the minimum support threshold.
 - **Candidate generation**: Iteratively generates candidate sequences of increasing length.
 - **General-purpose**: Useful in retail, web analytics, social networks, temporal sequence mining, and more.
 
 For example:
+
 - In a shopping dataset, GSP can identify patterns like "Customers who buy bread and milk often purchase diapers next."
 - In a website clickstream, GSP might find patterns like "Users visit A, then go to B, and later proceed to C."
 
 ---
 
-## ✅ Requirements
+## 🔧 Requirements
 
 You will need Python installed on your system. On most Linux systems, you can install Python with:
 
@@ -55,22 +62,30 @@ sudo apt install python3
 
 For package dependencies of GSP-Py, they will automatically be installed when using `pip`.
 
+> [!IMPORTANT]
+> GSP-Py is compatible with Python 3.11 and later versions.
+> We didn't test it on Python 3.10 or earlier versions.
+
 ---
 
 ## 🚀 Installation
 
-GSP-Py can be easily installed either by cloning the repository or using pip.
+GSP-Py can be easily installed from either the **repository** or PyPI.
 
 ### Option 1: Clone the Repository
+
 To manually clone the repository and install:
+
 ```bash
 git clone https://github.com/jacksonpradolima/gsp-py.git
 cd gsp-py
 python setup.py install
 ```
 
 ### Option 2: Install via `pip`
+
 Alternatively, install GSP-Py from PyPI with:
+
 ```bash
 pip install gsppy
 ```
@@ -79,9 +94,11 @@ pip install gsppy
 
 ## 🛠️ Developer Installation
 
-For contributors and developers, GSP-Py provides additional dependencies for development purposes (e.g., testing and linting).
+For contributors and developers, GSP-Py provides additional dependencies for development purposes (e.g., testing and
+linting).
 
 To install the package along with development dependencies, use:
+
 ```bash
 pip install .[dev]
 ```
@@ -90,10 +107,13 @@ The `dev` category includes tools such as `pytest`, `pylint`, and others to ensu
 
 ## 💡 Usage
 
-The library is designed to be easy to use and integrate with your own projects. Below is an example of how you can configure and run GSP-Py.
+The library is designed to be easy to use and integrate with your own projects. Below is an example of how you can
+configure and run GSP-Py.
 
 ### Example Input Data
+
 The input to the algorithm is a sequence of transactions, where each transaction contains a sequence of items:
+
 ```python
 transactions = [
     ['Bread', 'Milk'],
@@ -105,20 +125,23 @@ transactions = [
 ```
 
 ### Importing and Initializing the GSP Algorithm
-Import the `GSP` class from the `gsppy` package and call the `search` method to find frequent patterns with a support threshold (e.g., `0.3`):
+
+Import the `GSP` class from the `gsppy` package and call the `search` method to find frequent patterns with a support
+threshold (e.g., `0.3`):
+
 ```python
 from gsppy.gsp import GSP
 
-# Define the input data
+# Example transactions: customer purchases
 transactions = [
-    ['Bread', 'Milk'],
-    ['Bread', 'Diaper', 'Beer', 'Eggs'],
-    ['Milk', 'Diaper', 'Beer', 'Coke'],
-    ['Bread', 'Milk', 'Diaper', 'Beer'],
-    ['Bread', 'Milk', 'Diaper', 'Coke']
+    ['Bread', 'Milk'],  # Transaction 1
+    ['Bread', 'Diaper', 'Beer', 'Eggs'],  # Transaction 2
+    ['Milk', 'Diaper', 'Beer', 'Coke'],  # Transaction 3
+    ['Bread', 'Milk', 'Diaper', 'Beer'],  # Transaction 4
+    ['Bread', 'Milk', 'Diaper', 'Coke']  # Transaction 5
 ]
 
-# Minimum support set to 30%
+# Set minimum support threshold (30%)
 min_support = 0.3
 
 # Find frequent patterns
@@ -129,12 +152,35 @@ print(result)
 ```
 
 ### Output
+
 The algorithm will return a list of patterns with their corresponding support.
 
-### Understanding Support
-The **support** of a sequence is the fraction of total data-sequences that "contain" the sequence. For instance, if the pattern `[Bread, Milk]` appears in 3 out of 5 transactions, its support is `3 / 5 = 0.6`.
+Sample Output:
+
+```python
+[
+    {('Bread',): 4, ('Milk',): 4, ('Diaper',): 4, ('Beer',): 3, ('Coke',): 2},
+    {('Bread', 'Milk'): 3, ('Milk', 'Diaper'): 3, ('Diaper', 'Beer'): 3},
+    {('Bread', 'Milk', 'Diaper'): 2, ('Milk', 'Diaper', 'Beer'): 2}
+]
+```
 
-For more complex examples, find example scripts in the [`gsppy/tests`](gsppy/tests) folder.
+- The **first dictionary** contains single-item sequences with their frequencies (e.g., `('Bread',): 4` means "Bread"
+  appears in 4 transactions).
+- The **second dictionary** contains 2-item sequential patterns (e.g., `('Bread', 'Milk'): 3` means the sequence "
+  Bread → Milk" appears in 3 transactions).
+- The **third dictionary** contains 3-item sequential patterns (e.g., `('Bread', 'Milk', 'Diaper'): 2` means the
+  sequence "Bread → Milk → Diaper" appears in 2 transactions).
+
+> [!NOTE]
+> The **support** of a sequence is calculated as the fraction of transactions containing the sequence, e.g.,
+`[Bread, Milk]` appears in 3 out of 5 transactions → Support = `3 / 5 = 0.6` (60%).
+> This insight helps identify frequently occurring sequential patterns in datasets, such as shopping trends or user
+> behavior.
+
+
+> [!TIP]
+> For more complex examples, find example scripts in the [`gsppy/tests`](gsppy/tests) folder.
 
 ---
 
@@ -143,29 +189,35 @@ For more complex examples, find example scripts in the [`gsppy/tests`](gsppy/tes
 We are actively working to improve GSP-Py. Here are some exciting features planned for future releases:
 
 1. **Custom Filters for Candidate Pruning**:
-   - Enable users to define their own pruning logic during the mining process.
+    - Enable users to define their own pruning logic during the mining process.
 
 2. **Support for Preprocessing and Postprocessing**:
-   - Add hooks to allow users to transform datasets before mining and customize the output results.
+    - Add hooks to allow users to transform datasets before mining and customize the output results.
 
 3. **Support for Time-Constrained Pattern Mining**:
-   - Extend GSP-Py to handle temporal datasets by allowing users to define time constraints (e.g., maximum time gaps between events, time windows) during the sequence mining process.
-   - Enable candidate pruning and support calculations based on these temporal constraints.
+    - Extend GSP-Py to handle temporal datasets by allowing users to define time constraints (e.g., maximum time gaps
+      between events, time windows) during the sequence mining process.
+    - Enable candidate pruning and support calculations based on these temporal constraints.
 
-Want to contribute or suggest an improvement? [Open a discussion or issue!](https://github.com/jacksonpradolima/gsp-py/issues)
+Want to contribute or suggest an
+improvement? [Open a discussion or issue!](https://github.com/jacksonpradolima/gsp-py/issues)
 
 ---
 
 ## 🤝 Contributing
 
-We welcome contributions from the community! If you'd like to help improve GSP-Py, read our [CONTRIBUTING.md](CONTRIBUTING.md) guide to get started.
+We welcome contributions from the community! If you'd like to help improve GSP-Py, read
+our [CONTRIBUTING.md](CONTRIBUTING.md) guide to get started.
+
+Development dependencies (e.g., testing and linting tools) are included in the `dev` category in `setup.py`. To install
+these dependencies, run:
 
-Development dependencies (e.g., testing and linting tools) are included in the `dev` category in `setup.py`. To install these dependencies, run:
 ```bash
 pip install .[dev]
 ```
 
 ### General Steps:
+
 1. Fork the repository.
 2. Create a feature branch: `git checkout -b feature/my-feature`.
 3. Commit your changes: `git commit -m "Add my feature."`
@@ -177,10 +229,22 @@ Looking for ideas? Check out our [Planned Features](#planned-features) section.
 ---
 
 ## 📝 License
+
 This project is licensed under the terms of the **MIT License**. For more details, refer to the [LICENSE](LICENSE) file.
 
 ---
 
 ## 📖 Citation
 
 If GSP-Py contributed to your research or project that led to a publication, we kindly ask that you cite it as follows:
+
+```
+@misc{pradolima_gsppy,
+  author       = {Prado Lima, Jackson Antonio do},
+  title        = {{GSP-Py - Generalized Sequence Pattern algorithm in Python}},
+  month        = May,
+  year         = 2020,
+  doi          = {10.5281/zenodo.3333987},
+  url          = {https://doi.org/10.5281/zenodo.3333987}
+}
+```