FuzzyCMeans

class geoanalytics.clustering.FuzzyCMeans.FuzzyCMeans(dataframe)

Bases: object

About this algorithm

Description:

Fuzzy C-Means (FCM) is a soft clustering technique where each data point is assigned a membership score to all clusters, allowing partial membership instead of a hard assignment to a single cluster.

Parameters:

• dataframe (pd.DataFrame) – * where:

• The first two columns are coordinates.

• The rest of the columns contain the features used for clustering.*

Attributes:

• df (pd.DataFrame) – A copy of your original data, with the first two columns renamed to `’x’` and `’y’` to make things easy and clear during clustering

Execution methods

Calling from a Python program

       from geoanalytics.clustering import FuzzyCMeans

       import pandas as pd

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

       obj = FuzzyCMeans(df)

       output = obj.clustering(n_clusters=3)

       labels = output[0]

       fcm.centers = output[1]

       obj.save('FuzzyCMeansLabels.csv')


**Credits**

The complete program was written by M. Charan Teja under the supervision 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)

Performs fuzzy clustering on data.

-It excludes the ‘x’ and ‘y’ columns during clustering, and uses the remaining columns. -After clustering, it adds the cluster label (based on the highest membership score) back to the original coordinates

Parameters:

n_clusters – Number of clusters to form (default is 3).

Returns:

A tuple containing: - A DataFrame with ‘x’, ‘y’, and cluster labels for each data point (based on maximum membership). - A NumPy array with coordinates of the cluster centers.

save(outputFileLabels='FuzzyCMeansLabels.csv', outputFileCenters='FuzzyCMeansCenters.csv')