Agglomerative

class geoanalytics.clustering.Agglomerative.Agglomerative(dataframe)

Bases: object

About this algorithm

Description:

Agglomerative Clustering is a hierarchical clustering technique that recursively merges the closest pairs of clusters. This wrapper applies agglomerative clustering on feature-rich multidimensional data and supports runtime and memory usage tracking, along with label export.

Parameters:

• Dataset (pandas DataFrame) must be provided during object initialization.

• Clustering parameters can be passed to the run method.

Attributes:

• df (pd.DataFrame) – The input data with ‘x’, ‘y’ coordinates and features.

• labelsDF (pd.DataFrame) – DataFrame containing ‘x’, ‘y’, and assigned cluster labels.

• startTime, endTime (float) – Variables to track clustering execution time.

• memoryUSS, memoryRSS (float) – Memory usage of the clustering process in kilobytes.

Execution methods

Calling from a Python program

import pandas as pd

from geoanalytics.clustering import Agglomerative

df = pd.read_csv("input.csv")

ag = Agglomerative(df)

labels_df = ag.run(n_clusters=4)

ag.getRuntime()
ag.getMemoryUSS()
ag.getMemoryRSS()

ag.save('AgglomerativeLabels.csv')

Credits

This implementation was created by Raashika and revised by M.Charan Teja under the guidance of Professor Rage Uday Kiran.

getMemoryRSS()

Prints the memory usage (RSS) of the process in kilobytes.

getMemoryUSS()

Prints the memory usage (USS) of the process in kilobytes.

getRuntime()

Prints the total runtime of the clustering algorithm.

run(n_clusters=4)

Executes Agglomerative Clustering algorithm.

Parameters:

n_clusters – int, number of clusters to form (default: 4)

Returns:

labelsDF (pd.DataFrame) with columns [‘x’, ‘y’, ‘labels’]

save(outputFileLabels='AgglomerativeLabels.csv')

Saves the clustering result with labels to a CSV file.

Parameters:

outputFileLabels – str, filename for saving labels (default: ‘AgglomerativeLabels.csv’)