[ENHANCEMENT] : Algorithm Enhancements and Refactoring: Dijkstra, A* Search, and K-Means Clustering #5880
Description
What would you like to Propose?
Implement and enhance complex algorithms in Java for improved efficiency and functionality in our automation processes.
This proposal aims to integrate advanced algorithms into our system, enhancing performance and providing robust solutions to complex problems. By leveraging algorithms such as Dijkstra's, A*, and K-Means Clustering, we can optimize our data processing and decision-making capabilities.
Issue details
For New Implementations
-
Algorithm : Dijkstra's Algorithm
Problem Statement: Find the shortest path from a starting node to all other nodes in a weighted graph. -
Algorithm : A Search Algorithm*
Problem Statement: Find the shortest path from a start node to a goal node using heuristics. -
Algorithm Name: K-Means Clustering
Problem Statement: Partition a set of points into K clusters by minimizing the variance within each cluster.
For Algorithm Enhancements
- Dijkstra's Algorithm Enhancements:
Adding Tests: Implement unit tests to ensure the algorithm functions correctly across various graph structures.
Optimizing Logic: Use a priority queue for more efficient vertex selection.
Refactoring: Separate the graph representation and the algorithm into distinct classes for better structure.
- A Search Algorithm Enhancements:*
Adding Tests: Write tests for different heuristics to ensure accuracy.
Optimizing Logic: Experiment with different heuristics to improve performance.
Refactoring: Use design patterns to enhance readability and maintainability.
- K-Means Clustering Enhancements:
Adding Tests: Ensure the algorithm works correctly for various clustering scenarios.
Optimizing Logic: Improve the algorithm's efficiency by using techniques such as the Elbow Method for selecting K.
Refactoring: Organize the clustering logic into separate methods or classes for clarity.
### Issue Details
Dijkstra's Algorithm:
This algorithm calculates the shortest path in a graph with weighted edges, crucial for navigation systems.
Enhancements: Adding unit tests for various scenarios.
Implementing a priority queue for improved performance.
Refactoring code for better maintainability.
A Search Algorithm:*
Utilizes heuristics to find the optimal path in pathfinding scenarios, particularly in gaming and robotics.
Enhancements: Adding tests for different heuristic strategies.
Optimizing pathfinding logic.
Refactoring the implementation into classes based on heuristic strategies.
K-Means Clustering:
Groups data points into clusters based on similarity, widely used in data analysis and marketing.
Enhancements: Adding tests for various clustering scenarios.
Improving efficiency with advanced techniques.
Refactoring the logic for better organization.
Additional Information
This proposal aims to integrate these advanced algorithms into our existing framework to enhance the functionality and efficiency of our systems. Please find attached screenshots illustrating current performance metrics, which can be improved through the proposed implementations.