https://github.com/whizsid/kddbscan-rs

A rust library inspired by kDDBSCAN clustering algorithm
https://github.com/whizsid/kddbscan-rs

clustering data-science density-based-clustering deviation machine-learning-algorithms pinned

Last synced: about 1 month ago
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A rust library inspired by kDDBSCAN clustering algorithm

• Host: GitHub
• URL: https://github.com/whizsid/kddbscan-rs
• Owner: whizsid
• License: mit
• Created: 2020-05-30T14:26:12.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-06-13T15:19:37.000Z (over 4 years ago)
• Last Synced: 2024-09-10T11:38:31.420Z (2 months ago)
• Topics: clustering, data-science, density-based-clustering, deviation, machine-learning-algorithms, pinned
• Language: Rust
• Homepage:
• Size: 39.1 KB
• Stars: 4
• Watchers: 3
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# kddbscan-rs 

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Rust implementation of the kddbscan clustering algorithm.

From the authors of kDDBSCAN algorithm.

> Due to the adoption of global parameters, DBSCAN fails to

> identify clusters with different and varied densities. To

> solve the problem, this paper extends DBSCAN by exploiting

> a new density definition and proposes a novel algorithm

> called k -deviation density based DBSCAN (kDDBSCAN). Various

> datasets containing clusters with arbitrary shapes and

> different or varied densities are used to demonstrate the

> performance and investigate the feasibility and practicality

> of kDDBSCAN. The results show that kDDBSCAN performs

> better than DBSCAN.

[Read More](https://www.researchgate.net/publication/323424266_A_k_-Deviation_Density_Based_Clustering_Algorithm)

## Installation

Add `kddbscan` as a dependency in your `Cargo.toml` file

```toml

[dependencies]

kddbscan = "0.1.0"

```

## Usage

Implement `IntoPoint` trait on your point struct. And pass a vector of points to the `cluster` function.

```rust

use kddbscan::{cluster, IntoPoint, ClusterId};

pub struct Coordinate {

    pub x: f64,

    pub y: f64,

}

impl IntoPoint for Coordinate {

    fn get_distance(&self, neighbor: &Coordinate) -> f64 {

        ((self.x - neighbor.x).powi(2) + (self.y - neighbor.y).powi(2)).powf(0.5)

    }

}

fn main() {

    let mut coordinates: Vec = vec![];

    coordinates.push(Coordinate { x: 11.0, y: 12.0 });

    coordinates.push(Coordinate { x: 0.0, y: 0.0 });

    coordinates.push(Coordinate { x: 12.0, y: 11.0 });

    coordinates.push(Coordinate { x: 11.0, y: 9.0 });

    coordinates.push(Coordinate { x: 10.0, y: 8.0 });

    coordinates.push(Coordinate { x: 1.0, y: 2.0 });

    coordinates.push(Coordinate { x: 3.0, y: 1.0 });

    coordinates.push(Coordinate { x: 4.0, y: 4.0 });

    coordinates.push(Coordinate { x: 9.0, y: 0.0 });

    let clustered =  cluster(coordinates, 2, None, None);

}

```

## Showcase

This is the output of example project.

![Output of the kddbscan algorithm](./example/result.png)

## Contribution

All PRs and issues are welcome. and starts are also welcome.

## License

This project is under the MIT license and the algorithm is under the CC BY 4.0 license.