Constrained Hierarchical Agglomerative Clustering

This repository contains the implementation of the constrained linkage function for Constrained Hierarchical Agglomerative Clustering from the paper:

HEAT: Hierarchical-constrained Encoder-Assisted Time series clustering for fault detection in district heating substations
Jonne van Dreven, Abbas Cheddad, Ahmad Nauman Ghazi, Sadi Alawadi, Jad Al Koussa, Dirk Vanhoudt
Energy and AI, 21 (2025), 100548
DOI: 10.1016/j.egyai.2025.100548

If you use this library in academic or scientific work, please cite:

@article{van_Dreven-HEAT,
  title={HEAT: Hierarchical-constrained Encoder-Assisted Time series clustering for fault detection in district heating substations},
  volume={21},
  ISSN={2666-5468},
  DOI={10.1016/j.egyai.2025.100548},
  journal={Energy and AI},
  author={van Dreven, Jonne and Cheddad, Abbas and Ghazi, Ahmad Nauman and Alawadi, Sadi and Al Koussa, Jad and Vanhoudt, Dirk},
  year={2025},
  month=sep,
  pages={100548}
}

A NumPy-only hierarchical agglomerative clustering routine with soft constraints, returning a SciPy-compatible linkage matrix Z.

✨ Features

• Drop-in replacement for a constrained linkage routine supporting:
  • single, complete, average, weighted, centroid, median, ward
• Accepts either:
  • condensed 1-D distances (len n*(n-1)/2)
  • n×n square distance matrix
• Adds soft constraints:
  • Must-link / Cannot-link via a constraint matrix M
    • M[i,j] < 0 → encourages merging (must-link)
    • M[i,j] > 0 → discourages merging (cannot-link)
  • Min/max cluster size penalties (linear in violation amount)
    • Minimum adds a penalty when a merge would create a cluster smaller than the specified minimum.
    • Maximum adds a penalty when a merge would create a cluster larger than the specified maximum.
    • Penalty grows linearly with how far below/above the minimum/maximum the cluster size is.
  • Normalised distances
    • When normalize_distances=True, the penalties are relative to the [0, 1] normalized distance range, making them proportional regardless of the original distance scale.
• No SciPy dependency — output Z works with SciPy’s downstream tools.

---

🔌 Plug-and-play

constrained_linkage is a drop-in replacement for SciPy’s linkage function.

• No constraints? Works identically to scipy.cluster.hierarchy.linkage.
• With constraints? Adds powerful, flexible soft constraints with minimal code changes.
• Output is a SciPy-compatible linkage matrix Z, so you can keep using all SciPy tools (e.g., fcluster, dendrogram) unchanged.

---

🔧 Install

pip install constrained-linkage
# from source:
pip install "git+https://github.com/jonnevd/constrained-linkage"

---

🚀 Usage Examples

Below we illustrate must-link (negative penalties) and cannot-link (positive penalties) via the constraint matrix M.
All distances are optionally scaled to [0,1] when normalize_distances=True, so penalties are scale-free.

Semantics:

• M[i, j] < 0 → must-link (encourage merging i↔j)
• M[i, j] > 0 → cannot-link (discourage merging i↔j)

---

Example 1 — Must-link & Cannot-link constraints

import numpy as np
from constrained_linkage import constrained_linkage
from scipy.cluster import hierarchy
import matplotlib.pyplot as plt

# Four points in 1D (two well-separated pairs)
X = np.array([[0.0], [0.1], [10.0], [10.1]])
D = np.sqrt(((X[:, None, :] - X[None, :, :]) ** 2).sum(-1))

# Constraint matrix: must-link 0↔1, cannot-link 2↔3
M = np.zeros_like(D)
M[0, 1] = M[1, 0] = -0.6   # must-link (negative)
M[2, 3] = M[3, 2] =  0.6   # cannot-link (positive)

Z = constrained_linkage(
    D, method="average",
    constraint_matrix=M,
    normalize_distances=True
)

# Works seamlessly with SciPy tools
labels = hierarchy.fcluster(Z, 2, criterion="maxclust")
print("Partition with must-link(0,1) & cannot-link(2,3):", labels)

plt.figure(figsize=(6, 3))
hierarchy.dendrogram(Z, labels=[f"P{i}" for i in range(len(X))])
plt.title("Dendrogram — must-link(0,1), cannot-link(2,3)")
plt.tight_layout()
plt.show()

Example 2 — Enforcing a maximum cluster size

Discourage clusters larger than a threshold by adding a positive penalty above the maximum.

import numpy as np
from constrained_linkage import constrained_linkage
from scipy.cluster import hierarchy

# Six points in 1D (three tight pairs)
X = np.array([[0.0], [0.1], [5.0], [5.1], [10.0], [10.1]])
D = np.sqrt(((X[:, None, :] - X[None, :, :]) ** 2).sum(-1))

Z_max = constrained_linkage(
    D, method="average",
    max_cluster_size=2,     # soft cap
    max_penalty_weight=0.6, # stronger => avoids overgrown clusters
    normalize_distances=True
)

labels_max = hierarchy.fcluster(Z_max, 3, criterion="maxclust")
print("Partition with max_cluster_size=2:", labels_max)

Example 3 — Enforcing a minimum cluster size

When domain knowledge suggests small units should coalesce before analysis, use a minimum size prior to avoid singletons or small groups. Increasing the penalty weight strengthens this bias, as shown in the figure below.

import numpy as np
from constrained_linkage import constrained_linkage
from scipy.cluster import hierarchy

# Six points in 1D (three tight pairs)
X = np.array([[0.0], [0.1], [5.0], [5.1], [10.0], [10.1]])
D = np.sqrt(((X[:, None, :] - X[None, :, :]) ** 2).sum(-1))

Z_min = constrained_linkage(
    D, method="average",
    min_cluster_size=3,     # target minimum size
    min_penalty_weight=0.5, # stronger => merge undersized clusters earlier
    normalize_distances=True
)

labels_min = hierarchy.fcluster(Z_min, 2, criterion="maxclust")
print("Partition with min_cluster_size=3:", labels_min)