mcmststream 1.0.0

MCMSTStream: applying minimum spanning tree to KD-tree-based micro-clusters to define arbitrary-shaped clusters in streaming data

MCMSTStream

MCMSTStream is a streaming clustering algorithm based on Minimum Spanning Trees (MST) and KD-Tree–based micro-clusters.

Features

✨ Features

✔ Online streaming clustering
✔ Sliding window model
✔ KD-Tree accelerated micro-cluster formation
✔ Macro-cluster discovery via Minimum Spanning Tree
✔ Noise & outlier handling
✔ Visualization utilities
✔ Scikit-learn–compatible API (fit, partial_fit, predict, get_params, set_params)
✔ Supports incremental, real-time data processing

Installation

pip install mcmststream

Parameters

If you want to use amount-based sliding window assign WindowType.AMOUNT_BASED If you want to use time based sliding window, assign WindowType.TIME_BASED N: int -> Minimum number of points to form a cluster r: float -> Initial cluster radius r_threshold: float -> Radius increase/decrease threshold r_max: float -> Maximum cluster radius window_type: WindowType -> {WindowType.AMOUNT_BASED,WindowType.TIME_BASED window_size: int -> For amount-based: number of points in window verbose: bool {True, False}

Usage

import numpy as np
from sklearn.metrics.cluster import adjusted_rand_score
from sklearn.preprocessing import MinMaxScaler
from mcmstream import MCMSTStream, load_exclastar

# Load data 
X, y_true = load_exclastar()

# Normalize
scaler = MinMaxScaler()
X_scaled = scaler.fit_transform(X)
np.random.seed(42)

# Initialize with history keeping enabled
clusterer = MCMSTStream(
    W=270,  
    n_micro=2, 
    N=2,   
    r=0.14, 
    random_state=42,
    keep_history=True  # Enable history tracking
)
for i, point in enumerate(X_scaled):
        label = clusterer.partial_fit(point)
        
        # Visualize periodically
        if i % 20 == 0 and i > 0:
            print(f"\nStep {i}:")
            print(f"  Current label for this point: {label}")
            print(f"  Micro-clusters: {len(clusterer.micro_clusters)}")
            print(f"  Macro-clusters: {len([m for m in clusterer.macro_clusters if m['active']])}")
            if clusterer.keep_history:
                hist_labels = np.array(clusterer.history_labels_)
                print(f"  History labels (unique): {np.unique(hist_labels)}")
            
            clusterer.visualize(title=f"Step {i}")
    
ARI=adjusted_rand_score(y_true,clusterer.history_labels_)
print("ARI=%0.4f"%ARI)

Visualization

The package includes a built-in visualization function:

clusterer.visualize(title="MCMSTStream Clustering Result")

Evaluation

Calculates:

Silhouette Score

Calinski-Harabasz Index

Davies-Bouldin Index

ARI, NMI, V-Measure (if true labels provided)

metrics = clusterer.evaluate(true_labels=y_true)
print(metrics)

Citation

If you use this algorithm in research, please cite the corresponding paper.

Erdinç, B., Kaya, M., & Şenol, A. (2024). MCMSTStream: applying minimum spanning tree to KD-tree-based micro-clusters to define arbitrary-shaped clusters in streaming data. Neural Computing and Applications, 36(13), 7025-7042.

BibTeX

@article{erdincc2024mcmststream,
  title={MCMSTStream: applying minimum spanning tree to KD-tree-based micro-clusters to define arbitrary-shaped clusters in streaming data},
  author={Erdin{\c{c}}, Berfin and Kaya, Mahmut and {\c{S}}enol, Ali},
  journal={Neural Computing and Applications},
  volume={36},
  number={13},
  pages={7025--7042},
  year={2024},
  publisher={Springer}
}