# -*- coding: utf-8 -*-
"""
MAT-Tools: Python Framework for Multiple Aspect Trajectory Data Mining
The present application offers a tool, to support the user in the clustering of multiple aspect trajectory data.It integrates into a unique framework for multiple aspects trajectories and in general for multidimensional sequence data mining methods.
Copyright (C) 2022, MIT license (this portion of code is subject to licensing from source project distribution)
Created on Apr, 2024
Copyright (C) 2024, License GPL Version 3 or superior (see LICENSE file)
Authors:
- Tarlis Portela
- Yuri Santos
"""
import pandas as pd
import numpy as np
from sklearn.cluster import DBSCAN
from matclustering.core import SimilarityClustering
[docs]
class TSDBSCAN(SimilarityClustering):
"""
Trajectory Density-Based Spatial Clustering of Applications with Noise (DBSCAN) using similarity matrix.
The `TSDBSCAN` class implements the DBSCAN clustering algorithm, which is a
density-based clustering method designed to discover clusters in large spatial
datasets while effectively identifying noise. This implementation allows
configuration of key parameters such as epsilon (eps) and minimum samples.
References
----------
Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996, August). A density-
based algorithm for discovering clusters in large spatial databases with
noise. In Kdd (Vol. 96, No. 34, pp. 226-231).
<https://www.aaai.org/Papers/KDD/1996/KDD96-037.pdf>
Parameters
----------
eps : float or list of floats, optional
The maximum distance between two samples for them to be considered
as in the same neighborhood. Can also be a list for grid search.
min_samples : int, optional
The number of samples (or total weight) in a neighborhood for a point
to be considered as a core point. Default is 5.
random_state : int, optional
Seed for random number generation, primarily for compatibility
with other components. Default is 1.
n_jobs : int, optional
The number of jobs to run in parallel for both `fit` and
`predict`. Default is 1.
verbose : bool, optional
If True, prints verbose output during processing. Default is False.
Methods
-------
create(config=None):
Initializes and returns a DBSCAN model with the specified parameters.
"""
def __init__(self,
eps=0.5,
min_samples=5,
random_state=1, # Not used, only for compatibility
n_jobs=1,
verbose=False):
super().__init__('TSDBSCAN', random_state=random_state, n_jobs=n_jobs, verbose=verbose)
self.add_config(eps=eps,
min_samples=min_samples)
if isinstance(eps, list):
self.grid_search(eps, min_samples) # list of k values transform in a 2D configs
else:
self.grid = [[eps, min_samples]] # just one config
[docs]
def if_config(self, config=None):
if config == None:
config = [
self.config['eps'],
self.config['min_samples']
]
return config
[docs]
def create(self, config=None):
eps, min_samples = self.if_config(config)
return DBSCAN(eps=eps, min_samples=min_samples)