SHiP-framework 0.1.2

Center-based hierarchical clustering framework using 3 steps: compute a Similarity, build an Hierarchy, and lastly extract a clustering using a Partitioning method.

Similarity-Hierarchical-Partitioning (SHiP) Clustering Framework

This repository is the official implementation of the Similarity-Hierarchical-Partitioning (SHiP) clustering framework proposed in Ultrametric Cluster Hierarchies: I Want `em All! This framework provides a comprehensive approach to clustering by leveraging similarity trees, $(k,z)$-hierarchies, and various partitioning objective functions.

The whole project is implemented in C++ and Python bindings enable the usage within Python.

Overview

The SHiP framework operates in three main stages:

1. Similarity Tree Construction: A similarity tree is built for the given dataset. This tree represents the relationships and proximities between data points. Note that the default constructed tree corresponds to the $k$-center hierarchy (Section 3 in the paper).
2. $(k,z)$-Hierarchy Construction: Using the similarity tree, a $(k,z)$-hierarchy can be constructed. These hierarchies correlate to common center based clustering methods, as e.g., $k$-median or $k$-means (Section 4).
3. Partitioning: Finally, the data is partitioned based on the constructed hierarchy and a user-selected partitioning objective function (Section 5).

Features

• Similarity Trees: The package provides a set of similarity/ultrametric tree implementations:

  • DCTree [1]
  • HST [2]
  • CoverTree [3]
  • KDTree [3]
  • MeanSplitKDTree [3]
  • BallTree [3]
  • MeanSplitBallTree [3]
  • RPTree [3]
  • MaxRPTree [3]
  • UBTree [3]
  • RTree [3]
  • RStarTree [3]
  • XTree [3]
  • HilbertRTree [3]
  • RPlusTree [3]
  • RPlusPlusTree [3]
  • Or use LoadTree to load a precomputed tree

• $(k,z)$-Hierarchies: It supports all possible $(k,z)$-hierarchies, allowing flexibility in choosing the most suitable hierarchy for a given dataset.

  • $z = 0$ → $k$-center (actually in theory: $z = ∞$, but in this implementation we use 0 for $∞$)
  • $z = 1$ → $k$-median
  • $z = 2$ → $k$-means
  • ...

• Partitioning Functions: A wide range of partitioning functions are available, enabling users to select the most appropriate function based on their specific needs:

  • K
  • Elbow
  • Threshold
  • ThresholdElbow
  • QCoverage
  • QCoverageElbow
  • QStem
  • QStemElbow
  • LcaNoiseElbow
  • LcaNoiseElbowNoTriangle
  • MedianOfElbows
  • MeanOfElbows
  • Stability
  • NormalizedStability

• Customization: Users can customize the framework by selecting from the available similarity trees, $(k,z)$- hierarchies, and partitioning functions.

  • E.g., DCTree with $k$-means ($z=2$)-hierarchy and the Elbow partitioning method.

    from SHiP import SHiP
    
    # Build the `DCTree`
    ship = SHiP(data=data_points, treeType="DCTree")
    # Extract the clustering from the $k$-median hierarchy and the `Elbow` partitioning method
    labels = ship.fit_predict(hierarchy=2, partitioningMethod="Elbow")
    

Installation

Stable Version

The current stable version can be installed by the following command:
pip install SHiP-framework (coming soon)

Note that a gcc compiler is required for installation. Therefore, in case of an installation error, make sure that:

• Windows: Microsoft C++ Build Tools is installed
• Linux/Mac: Python dev is installed (e.g., by running apt-get install python-dev - the exact command may differ depending on the linux distribution)

The error messages may look like this:

error: command 'gcc' failed: No such file or directory
Could not build wheels for SHiP-framework, which is required to install pyproject.toml-based projects
Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools

Development Version

The current development version can be installed directly from git by executing:
sudo pip install git+https://github.com/pasiweber/SHiP-framework.git

Alternatively, clone the repository, go to the root directory and execute:
pip install .

Code Example

from SHiP import SHiP

ship = SHiP(data=data, treeType="DCTree")

# or to load a saved tree
ship = SHiP(data=data, treeType="LoadTree", config={"json_tree_filepath": "<file_path>"}) 
# or additionally specify the tree_type of the loaded tree by adding {"tree_type": "DCTree"}

ship.hierarchy = 0
ship.partitioningMethod = "K"
labels = ship.fit_predict()

# or in one line
labels = ship.fit_predict(hierarchy = 1, partitioningMethod = "Elbow")

# optional: save the current computed tree
json = ship.get_tree().to_json()

Results

Our framework achieves the following performance:

Dataset	DC-0-Stab.	DC-1-MoE	DC-2-Elb.	CT-0-Stab.	CT-1-MoE	CT-2-Elb.	$k$-means	SCAR	Ward	AMD-DBSCAN	DPC
Boxes	90.1	99.3	97.9	2.6	42.1 ± 4.7	24.2 ± 1.6	93.5 ± 4.3	0.1 ± 0.1	95.8	63.9	25.9
D31	79.7	42.7	82.9	46.5 ± 1.8	62.0 ± 5.4	67.7 ± 3.2	92.0 ± 2.7	41.7 ± 5.4	92.0	86.4	18.5
airway	38.0	65.9	58.8	0.8	18.2 ± 2.4	12.0 ± 1.4	39.9 ± 2.0	-0.9 ± 0.5	43.7	31.7	65.1
lactate	41.0	41.0	67.5	0.1	4.1 ± 0.6	1.7 ± 0.2	28.6 ± 1.1	1.5 ± 1.0	27.7	71.5	0.0
HAR	30.0	46.9	52.8	14.7 ± 8.8	14.2 ± 4.7	9.6 ± 2.2	46.0 ± 4.5	5.5 ± 3.2	49.1	0.0	33.2
letterrec.	12.1	16.6	17.9	5.8 ± 0.2	7.2 ± 0.6	6.2 ± 0.3	12.9 ± 0.6	0.4 ± 0.1	14.7 ± 0.9	7.9	0.0
PenDigits	66.4	73.1	75.4	8.0 ± 0.8	12.0 ± 0.6	8.9 ± 0.5	55.3 ± 3.2	0.9 ± 0.3	55.2	55.6	28.8 ± 1.1
COIL20	81.2	72.8	72.6	46.4 ± 4.4	46.6 ± 2.1	47.7 ± 2.0	58.2 ± 2.8	33.5 ± 2.0	68.6	39.2	35.9 ± 0.1
COIL100	80.1	66.8	70.0	44.6 ± 4.2	46.6 ± 1.5	50.1 ± 1.2	56.1 ± 1.4	16.7 ± 0.8	61.4	14.2	0.2
cmu_faces	60.2	56.6	66.5	8.6 ± 3.1	37.1 ± 4.1	34.2 ± 2.1	53.2 ± 4.7	38.5 ± 2.9	61.6	0.7	0.6
OptDigits	55.3	77.0	77.0	40.9 ± 3.5	20.9 ± 2.3	18.1 ± 2.4	61.3 ± 6.6	14.4 ± 4.1	74.6 ± 2.4	63.2	0.0
USPS	33.7	29.3	29.3	12.0 ± 1.7	8.7 ± 1.0	11.2 ± 1.5	52.3 ± 1.7	2.9 ± 0.9	63.9	0.0	21.0
MNIST	19.7	41.7	46.0	11.1 ± 1.7	5.4 ± 0.6	5.4 ± 0.6	36.9 ± 1.0	1.3 ± 0.4	52.7	0.0	-

• DC = DCTree, CT = CoverTree
• Stab. = Stability, MoE = MedianOfElbows, Elb. = Elbow
• Competitors: k-means, SCAR, Ward, AMD-DBSCAN, DPC

License

The project is licensed under the BSD 3-Clause License (see LICENSE.txt).

References

[1] Connecting the Dots -- Density-Connectivity Distance unifies DBSCAN, k-Center and Spectral Clustering
[2] HST+: An Efficient Index for Embedding Arbitrary Metric Spaces (Github)
[3] mlpack 4: a fast, header-only C++ machine learning library (Github)