[−][src]Crate kddbscan
A k -Deviation Density Based Clustering Algorithm (kDDBSCAN)
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.
Installation
Add the following to your Cargo.toml
file:
[dependencies]
kddbscan = "0.1.0"
Usage
use kddbscan::{cluster, IntoPoint}; pub struct Coordinate { pub x: f64, pub y: f64, } // Implement IntoPoint trait to your data structur 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() { // Create a vector with your data let mut coordinates: Vec<Coordinate> = 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: 11.0 }); coordinates.push(Coordinate { x: 1.0, y: 2.0 }); coordinates.push(Coordinate { x: 3.0, y: 1.0 }); // Call cluster function let clustered = cluster(coordinates, 2, None, None); let first_cluster_id = clustered.get(0).unwrap().get_cluster_id(); let second_cluster_id = clustered.get(1).unwrap().get_cluster_id(); assert_eq!(first_cluster_id, clustered.get(2).unwrap().get_cluster_id()); assert_eq!(first_cluster_id, clustered.get(3).unwrap().get_cluster_id()); assert_eq!(second_cluster_id, clustered.get(4).unwrap().get_cluster_id()); assert_eq!(second_cluster_id, clustered.get(5).unwrap().get_cluster_id()); }
Structs
PointWrapper | This struct is using to store temporary values for points such as cluster_id and index of the point |
Enums
ClusterId | Cluster id types |
Traits
IntoPoint | You should implement |
Functions
cluster | Clustering a vec of structs |