kluster-fudge 0.1.0

A Python library for clustering categorical data using K-Modes with various distance metrics.

KlusterFudge

A Python library for clustering categorical data using the K-Modes algorithm. It supports multiple initialization methods and distance metrics, optimized with numba for performance.

Features

• Algorithms: K-Modes clustering for categorical data.
• Distance Metrics: Hamming, Jaccard, and NG dissimilarity measures.
• Initialization: Random, Huang, and Cao methods.
• Optimization: Computationally intensive operations are accelerated using numba.
• Integration: Supports all ArrayLikes (e.g. numpy arrays, pandas DataFrames, lists of lists, etc.)

Installation

Install KlusterFudge via pip:

pip install kluster-fudge

Or install from source:

git clone https://github.com/ethqnol/ClusterFudge.git
cd ClusterFudge
pip install .

Quick Start

import pandas as pd
from kluster_fudge import KModes

# 1. Load Data
df = pd.DataFrame({
    'color': ['red', 'blue', 'red', 'green', 'blue', 'green'],
    'size': ['small', 'large', 'small', 'large', 'medium', 'medium'],
    'shape': ['circle', 'square', 'circle', 'square', 'triangle', 'triangle']
})

# 2. Initialize Model
model = KModes(
    n_clusters=2, 
    init_method='cao', 
    dist_metric='hamming', 
    random_state=42
)

# 3. Fit and Predict
clusters = model.fit_predict(df)
print("Cluster Labels:", clusters)

Comparisons & Benchmarks

See benchmarks/benchmark_metrics.py

Metric	Init	Time (s)
jaccard	cao	0.051574
jaccard	huang	0.051677
hamming	cao	0.052091
hamming	huang	0.052477
ng	huang	0.055497
ng	cao	0.055761
jaccard	random	0.569103
ng	random	1.325702
hamming	random	3.131030

(Run on 5000 samples, 20 features, 10 categories)

License

MIT License