Function appr_dbscan::do_appr_dbscan_auto_dimensionality_file

pub fn do_appr_dbscan_auto_dimensionality_file<P>(
    filename: P, 
    epsilon: f64, 
    rho: f64, 
    min_pts: usize
) -> (VectorDBSCANResult, usize) where
    P: AsRef<Path>, 

Function that returns the result of the approximate DBSCAN algorithm without prior knowledge of the points dimensionality , executed on the set of points contained in filename with the given values of epsilon and rho.

Arguments

• filename: the path to the file containing the data points. The file should be formatted with one point per line and the values for each coordinate should be separated by a white space. Only numerical coordinates values are accepted.
• epsilon: the radius for the DBSCAN algorithm.
• rho: the approximation factor. The smaller it is the more precise the result. Usual values are 0.1 and 0.01.
• min_pts: the minimum number of nearby points required by the DBSCAN algorithm to declare an area as 'dense'.

Return value

This function returns a vector of clusters, where each cluster is a vector of the points contained in it. Each point is stored as a vector of f64, contrary to the other functions, along with the detected dimensionality of the points inside. The element at index 0 is the collection of all noise points, while all the other elements are the actual clusters.

Example

extern crate appr_dbscan;
use appr_dbscan::do_appr_dbscan_auto_dimensionality_file;

let (res,dimensionality) = do_appr_dbscan_auto_dimensionality_file("./datasets/out_test_1.txt", 0.3, 0.1, 10);
let clusters_count = res.len() - 1;
let noise_points_count = res[0].len();