# -*- 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 Birch
from matclustering.core import SimilarityClustering
[docs]
class TSBirch(SimilarityClustering):
"""
BIRCH Clustering for trajectory data using a similarity matrix.
Parameters
----------
k : int or list of int, optional
The number of clusters to find. Can be a single value or a list of values for grid search.
random_state : int, default=1
Seed for reproducibility. Not actively used in the BIRCH algorithm.
n_jobs : int, default=1
Number of parallel jobs to run. Default is 1 (no parallelism).
verbose : bool, default=False
If True, enables verbose output during the clustering process.
Methods
-------
create(config=None)
Creates an instance of the Birch model using the provided configuration.
"""
def __init__(self,
k=None,
random_state=1, # Not used, only for compatibility
n_jobs=1,
verbose=False):
super().__init__('TSBirch', random_state=random_state, n_jobs=n_jobs, verbose=verbose)
self.add_config(n_clusters=k)
if isinstance(k, list):
self.grid_search(k) # list of k values transform in a 2D configs
else:
self.grid = [[k]] # just one config
[docs]
def if_config(self, config=None):
if config == None:
config = [
self.config['n_clusters']
]
return config
[docs]
def create(self, config=None):
n_clusters, = self.if_config(config)
return Birch(n_clusters=n_clusters)