# Similarity-Hierarchical-Partitioning (SHiP) Clustering Framework
[](https://pypi.org/project/SHiP-framework/)
[](https://github.com/pasiweber/SHiP-framework/actions/workflows/publish_to_pypi.yml)
[](https://SHiP-framework.readthedocs.io/en/)
This repository is the official implementation of the Similarity-Hierarchical-Partitioning (SHiP) clustering framework proposed in [Ultrametric Cluster Hierarchies: I Want `em All!](https://github.com/pasiweber/SHiP-framework/) 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:
[](https://github.com/pasiweber/SHiP-framework/)
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]](#references)
- `HST` [[2]](#references)
- `CoverTree` [[3]](#references)
- `KDTree` [[3]](#references)
- `MeanSplitKDTree` [[3]](#references)
- `BallTree` [[3]](#references)
- `MeanSplitBallTree` [[3]](#references)
- `RPTree` [[3]](#references)
- `MaxRPTree` [[3]](#references)
- `UBTree` [[3]](#references)
- `RTree` [[3]](#references)
- `RStarTree` [[3]](#references)
- `XTree` [[3]](#references)
- `HilbertRTree` [[3]](#references)
- `RPlusTree` [[3]](#references)
- `RPlusPlusTree` [[3]](#references)
- 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.
```python
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:<br/>
`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](https://visualstudio.microsoft.com/de/visual-cpp-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:<br/>
`sudo pip install git+https://github.com/pasiweber/SHiP-framework.git`
Alternatively, clone the repository, go to the root directory and execute:<br/>
`pip install .`
## Code Example
```python
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](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html), [SCAR](https://github.com/SpectralClusteringAcceleratedRobust/SCAR), [Ward](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html), [AMD-DBSCAN](https://github.com/AlexandreWANG915/AMD-DBSCAN), [DPC](https://github.com/colinwke/dpca)
## License
The project is licensed under the BSD 3-Clause License (see [LICENSE.txt](https://github.com/pasiweber/SHiP-framework/blob/main/LICENSE.txt)).
## References
[1] [Connecting the Dots -- Density-Connectivity Distance unifies DBSCAN, k-Center and Spectral Clustering](https://epub.ub.uni-muenchen.de/123737/)
<br>
[2] [HST+: An Efficient Index for Embedding Arbitrary Metric Spaces](https://ieeexplore.ieee.org/document/9458703/)
([Github](https://github.com/yzengal/ICDE21-HST))
<br>
[3] [mlpack 4: a fast, header-only C++ machine learning library](https://joss.theoj.org/papers/10.21105/joss.05026)
([Github](https://github.com/mlpack/mlpack))
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"description": "# Similarity-Hierarchical-Partitioning (SHiP) Clustering Framework\n[](https://pypi.org/project/SHiP-framework/)\n[](https://github.com/pasiweber/SHiP-framework/actions/workflows/publish_to_pypi.yml)\n[](https://SHiP-framework.readthedocs.io/en/)\n\nThis repository is the official implementation of the Similarity-Hierarchical-Partitioning (SHiP) clustering framework proposed in [Ultrametric Cluster Hierarchies: I Want `em All!](https://github.com/pasiweber/SHiP-framework/) This framework provides a comprehensive approach to clustering by leveraging similarity trees, $(k,z)$-hierarchies, and various partitioning objective functions. \n\nThe whole project is implemented in C++ and Python bindings enable the usage within Python.\n\n\n## Overview\nThe SHiP framework operates in three main stages:\n[](https://github.com/pasiweber/SHiP-framework/)\n\n1. **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).\n2. **$(k,z)$-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).\n3. **Partitioning:** Finally, the data is partitioned based on the constructed hierarchy and a user-selected partitioning objective function (Section 5).\n\n\n## Features\n- **Similarity Trees:** The package provides a set of similarity/ultrametric tree implementations:\n - `DCTree` [[1]](#references)\n - `HST` [[2]](#references)\n - `CoverTree` [[3]](#references)\n - `KDTree` [[3]](#references)\n - `MeanSplitKDTree` [[3]](#references)\n - `BallTree` [[3]](#references)\n - `MeanSplitBallTree` [[3]](#references)\n - `RPTree` [[3]](#references)\n - `MaxRPTree` [[3]](#references)\n - `UBTree` [[3]](#references)\n - `RTree` [[3]](#references)\n - `RStarTree` [[3]](#references)\n - `XTree` [[3]](#references)\n - `HilbertRTree` [[3]](#references)\n - `RPlusTree` [[3]](#references)\n - `RPlusPlusTree` [[3]](#references)\n - Or use `LoadTree` to load a precomputed tree\n\n\n- **$(k,z)$-Hierarchies:** It supports all possible $(k,z)$-hierarchies, allowing flexibility in choosing the most suitable hierarchy for a given dataset.\n - $z = 0$ → $k$-center (actually in theory: $z = \u221e$, but in this implementation we use 0 for $\u221e$)\n - $z = 1$ → $k$-median\n - $z = 2$ → $k$-means\n - ...\n\n- **Partitioning Functions:** A wide range of partitioning functions are available, enabling users to select the most appropriate function based on their specific needs:\n - `K`\n - `Elbow`\n - `Threshold`\n - `ThresholdElbow`\n - `QCoverage`\n - `QCoverageElbow`\n - `QStem`\n - `QStemElbow`\n - `LcaNoiseElbow`\n - `LcaNoiseElbowNoTriangle`\n - `MedianOfElbows`\n - `MeanOfElbows`\n - `Stability`\n - `NormalizedStability`\n\n- **Customization:** Users can customize the framework by selecting from the available similarity trees, $(k,z)$-\nhierarchies, and partitioning functions.\n - E.g., `DCTree` with $k$-means ($z=2$)-hierarchy and the `Elbow` partitioning method.\n ```python\n from SHiP import SHiP\n\n # Build the `DCTree`\n ship = SHiP(data=data_points, treeType=\"DCTree\")\n # Extract the clustering from the $k$-median hierarchy and the `Elbow` partitioning method\n labels = ship.fit_predict(hierarchy=2, partitioningMethod=\"Elbow\")\n ```\n\n\n## Installation\n### Stable Version\nThe current stable version can be installed by the following command:<br/>\n`pip install SHiP-framework` (coming soon)\n\nNote that a gcc compiler is required for installation.\nTherefore, in case of an installation error, make sure that:\n- Windows: [Microsoft C++ Build Tools](https://visualstudio.microsoft.com/de/visual-cpp-build-tools/) is installed\n- 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)\n\nThe error messages may look like this:\n```\nerror: command 'gcc' failed: No such file or directory\nCould not build wheels for SHiP-framework, which is required to install pyproject.toml-based projects\nMicrosoft Visual C++ 14.0 or greater is required. Get it with \"Microsoft C++ Build Tools\n```\n\n\n### Development Version\nThe current development version can be installed directly from git by executing:<br/>\n`sudo pip install git+https://github.com/pasiweber/SHiP-framework.git`\n\nAlternatively, clone the repository, go to the root directory and execute:<br/>\n`pip install .`\n\n\n## Code Example\n```python\nfrom SHiP import SHiP\n\nship = SHiP(data=data, treeType=\"DCTree\")\n\n# or to load a saved tree\nship = SHiP(data=data, treeType=\"LoadTree\", config={\"json_tree_filepath\": \"<file_path>\"}) \n# or additionally specify the tree_type of the loaded tree by adding {\"tree_type\": \"DCTree\"}\n\nship.hierarchy = 0\nship.partitioningMethod = \"K\"\nlabels = ship.fit_predict()\n\n# or in one line\nlabels = ship.fit_predict(hierarchy = 1, partitioningMethod = \"Elbow\")\n\n# optional: save the current computed tree\njson = ship.get_tree().to_json()\n```\n\n\n## Results\nOur framework achieves the following performance:\n\nDataset | 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 |\n|-|-|-|-|-|-|-|-|-|-|-|-|\n| Boxes | 90.1 | **99.3** | _97.9_ | 2.6 | 42.1 \u00b1 4.7 | 24.2 \u00b1 1.6 | 93.5 \u00b1 4.3 | 0.1 \u00b1 0.1 | 95.8 | 63.9 | 25.9 |\n| D31 | 79.7 | 42.7 | 82.9 | 46.5 \u00b1 1.8 | 62.0 \u00b1 5.4 | 67.7 \u00b1 3.2 | **92.0 \u00b1 2.7** | 41.7 \u00b1 5.4 | **92.0** | _86.4_ | 18.5 |\n| airway | 38.0 | **65.9** | 58.8 | 0.8 | 18.2 \u00b1 2.4 | 12.0 \u00b1 1.4 | 39.9 \u00b1 2.0 | -0.9 \u00b1 0.5 | 43.7 | 31.7 | _65.1_ |\n| lactate | 41.0 | 41.0 | _67.5_ | 0.1 | 4.1 \u00b1 0.6 | 1.7 \u00b1 0.2 | 28.6 \u00b1 1.1 | 1.5 \u00b1 1.0 | 27.7 | **71.5** | 0.0 |\n| HAR | 30.0 | 46.9 | **52.8** | 14.7 \u00b1 8.8 | 14.2 \u00b1 4.7 | 9.6 \u00b1 2.2 | 46.0 \u00b1 4.5 | 5.5 \u00b1 3.2 | _49.1_ | 0.0 | 33.2 |\n| letterrec. | 12.1 | _16.6_ | **17.9** | 5.8 \u00b1 0.2 | 7.2 \u00b1 0.6 | 6.2 \u00b1 0.3 | 12.9 \u00b1 0.6 | 0.4 \u00b1 0.1 | 14.7 \u00b1 0.9 | 7.9 | 0.0 |\n| PenDigits | 66.4 | _73.1_ | **75.4** | 8.0 \u00b1 0.8 | 12.0 \u00b1 0.6 | 8.9 \u00b1 0.5 | 55.3 \u00b1 3.2 | 0.9 \u00b1 0.3 | 55.2 | 55.6 | 28.8 \u00b1 1.1 |\n| COIL20 | **81.2** | _72.8_ | 72.6 | 46.4 \u00b1 4.4 | 46.6 \u00b1 2.1 | 47.7 \u00b1 2.0 | 58.2 \u00b1 2.8 | 33.5 \u00b1 2.0 | 68.6 | 39.2 | 35.9 \u00b1 0.1 |\n| COIL100 | **80.1** | 66.8 | _70.0_ | 44.6 \u00b1 4.2 | 46.6 \u00b1 1.5 | 50.1 \u00b1 1.2 | 56.1 \u00b1 1.4 | 16.7 \u00b1 0.8 | 61.4 | 14.2 | 0.2 |\n| cmu_faces | 60.2 | 56.6 | **66.5** | 8.6 \u00b1 3.1 | 37.1 \u00b1 4.1 | 34.2 \u00b1 2.1 | 53.2 \u00b1 4.7 | 38.5 \u00b1 2.9 | _61.6_ | 0.7 | 0.6 |\n| OptDigits | 55.3 | **77.0** | **77.0** | 40.9 \u00b1 3.5 | 20.9 \u00b1 2.3 | 18.1 \u00b1 2.4 | 61.3 \u00b1 6.6 | 14.4 \u00b1 4.1 | _74.6 \u00b1 2.4_ | 63.2 | 0.0 |\n| USPS | 33.7 | 29.3 | 29.3 | 12.0 \u00b1 1.7 | 8.7 \u00b1 1.0 | 11.2 \u00b1 1.5 | _52.3 \u00b1 1.7_ | 2.9 \u00b1 0.9 | **63.9** | 0.0 | 21.0 |\n| MNIST | 19.7 | 41.7 | _46.0_ | 11.1 \u00b1 1.7 | 5.4 \u00b1 0.6 | 5.4 \u00b1 0.6 | 36.9 \u00b1 1.0 | 1.3 \u00b1 0.4 | **52.7** | 0.0 | - |\n\n- `DC = DCTree`, `CT = CoverTree`\n- `Stab. = Stability`, `MoE = MedianOfElbows`, `Elb. = Elbow`\n- Competitors: [k-means](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html), [SCAR](https://github.com/SpectralClusteringAcceleratedRobust/SCAR), [Ward](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html), [AMD-DBSCAN](https://github.com/AlexandreWANG915/AMD-DBSCAN), [DPC](https://github.com/colinwke/dpca)\n\n\n## License\nThe project is licensed under the BSD 3-Clause License (see [LICENSE.txt](https://github.com/pasiweber/SHiP-framework/blob/main/LICENSE.txt)).\n\n\n## References\n[1] [Connecting the Dots -- Density-Connectivity Distance unifies DBSCAN, k-Center and Spectral Clustering](https://epub.ub.uni-muenchen.de/123737/)\n<br>\n[2] [HST+: An Efficient Index for Embedding Arbitrary Metric Spaces](https://ieeexplore.ieee.org/document/9458703/)\n([Github](https://github.com/yzengal/ICDE21-HST))\n<br>\n[3] [mlpack 4: a fast, header-only C++ machine learning library](https://joss.theoj.org/papers/10.21105/joss.05026) \n([Github](https://github.com/mlpack/mlpack))\n",
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