mcmststream 1.2.3

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

MCMSTStream

MCMSTStream is a density-based stream clustering algorithm that applies a Minimum Spanning Tree (MST) over KD-tree-based micro-clusters to define arbitrary-shaped clusters in streaming data.

Published in Neural Computing and Applications (2024) 36:7025–7042.

Key features

• Online (sliding-window) stream clustering
• Detects arbitrary-shaped (non-spherical) clusters
• Robust to outliers and noisy data
• Handles high-dimensional streams via KD-tree range search
• Scikit-learn-style API: fit, partial_fit, fit_predict, predict, get_params, set_params

Installation

pip install mcmststream

For the built-in visualization support:

pip install "mcmststream[viz]"

Quick start

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

# Load the bundled ExclaStar benchmark dataset
X, y_true = load_exclastar()
X = MinMaxScaler().fit_transform(X)

clusterer = MCMSTStream(
    W=190,        # sliding window width
    N=2,          # min points to define a micro-cluster
    r=0.23,       # micro-cluster radius
    n_micro=2,    # min micro-clusters to define a macro-cluster
    dense_mc_promotion=True,
    random_state=42,
    keep_history=True,
)

# Process the stream point by point
for point in X:
    clusterer.partial_fit(point)
    clusterer.visualize(title="MCMSTStream on ExclaStar") # to see step-by-step visual results
    

print("ARI = %.4f" % adjusted_rand_score(y_true, clusterer.history_labels_))

Optional visualization of the current window:

clusterer.visualize(title="MCMSTStream on ExclaStar")

Parameters

Parameter	Description	Paper symbol
W	Sliding window width	W
N	Minimum number of points within radius r to define a micro-cluster	N
r	Micro-cluster radius; micro-clusters within 2r are linked by the MST	r
n_micro	Minimum number of micro-clusters to form a macro-cluster	n_micro
dense_mc_promotion	If True, a component whose total point count is ≥ n_micro * N also becomes a macro-cluster even with fewer than n_micro micro-clusters (extension beyond the paper; default False)	—

How it works

1. Micro-clusters are formed with a KD-tree range search: at least N points within radius r.
2. Macro-clusters are built by running Prim's MST over micro-cluster centers, connecting those within 2r.
3. As the stream evolves, micro/macro-clusters are updated, merged, or deleted, adapting to concept drift.

Citation

If you use this package in your research, please cite:

@article{erdinc2024mcmststream,
  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},
  doi     = {10.1007/s00521-024-09443-1}
}

Related projects

• MCMSTClustering — the batch (static data) counterpart
• KD-AR Stream — KD-tree and adaptive radius stream clustering

License

MIT — see LICENSE.