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Weighted KMeans Clustering for Geolocational Problem

Project description

Repo for weighted k means clustering for specifically geo locational problems.

For an example and mathematical explanation:

https://emrahcimren.github.io/data%20science/Greenfield-Analysis-with-Weighted-Clustering/

Prerequisites

Install environment.yml for prerequisites.

` conda env create -f environment.yml `

To recreate environment.yml

` conda env export > environment.yml `

To create requirements.txt from environment.yml

` pip freeze > requirements.txt `

Installation

` pip install cimren-wkmeans-geo `

Inputs

input_locations is a pandas dataframe with the following format.

LOCATION_NAME | LATITUDE | LONGITUDE | WEIGHT — | — | — | — LOC 0 | -27.0065 | 170.583 | 1

number_of_clusters: Number of clusters to be created

minimum_elements_in_a_cluster: Minimum elements in a cluster

maximum_elements_in_a_cluster: Maximum elements in a cluster

maximum_iteration: How many maximum number of steps the algorithm takes to stop if it does not find the solution

enable_minimum_maximum_elements_in_a_cluster: True/False to enable minimum and maximum cluster size

objective_range: Acceptable difference between objectives at each iteration

How to use

Create initial clusters from a given solution. - Number of clusters to be generated should be bigger than number of clusters in the input solution

` from wkmeans_geo import clusters_from_input as cf initial_solution = cf.create_initial_clusters_from_given_input(number_of_clusters, input_locations_with_clusters) `

Create clusters.

` from wkmeans_geo import wkmeans_clustering as wkc clusters, locations_with_clusters = wkc.calculate_clusters(...) `

Project details


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