| Name | ISCT JSON |
| Version |
0.1.2
JSON |
| download |
| home_page | None |
| Summary | Interpretable Sequence Clustering Tree |
| upload_time | 2025-01-12 15:05:14 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | None |
| license | None |
| keywords |
isct
sequence
clustering
tree
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Interpretable-Sequence-Clustering-Tree
Source Code for Interpretable sequence clustering https://www.sciencedirect.com/science/article/pii/S0020025524013677
## Running
We added the tree structure visualization, to use it on linux please first
```
sudo apt-get install graphviz
export PATH=$PATH:/usr/local/bin
source ~/.bashrc
```
We recommend using Pypy, which may give exponential speedups on larger datasets
## Usage
### Random Projection on Python version
```
pip install ISCT
```
<!-- code -->
```python
from ISCT import ISCT
sequences = [
['a', 'g', 't', 't', 'c'],
['a', 't', 'g', 'g', 't', 'u', 't'],
['a', 'c', 't', 'u', 'u', 'a', 'a'],
['a', 'c', 'a', 'u', 'a', 't', 'c', 't'],
['a', 'g', 'g', 'c', 'a', 'a', 'c'],
['a', 'c', 'g', 'g', 'c', 'c', 'a', 'a']]
isct = ISCT(num_clusters = 3, visulization_name = 'test', min_sample=2) # if visulization_name is provided, it generates the "test.pdf".
y_pred = isct.fit(sequences)
print(y_pred)
```
<!--
### Random Projection CPP version
1. First, compile the cpp file on your Linux in order to generate the fast Random Projection Generator.
2. Run ISCT_cpp.py to get the clustering results -->
## Dependencies
- Python 3.9.16 (Pypy)
- NumPy 1.24.3
- Scikit-learn 1.2.2
- Pandas 2.0.1
- Prefixspan 0.5.2
## Visualization
ISCT could provide you with a highly concise and short clustering tree, taking poineer as example:
<img width="408" alt="image" src="https://github.com/jd445/Interpretable-Sequence-Clustering-Tree/assets/65555729/5a0a465f-0d7d-4d5c-a149-9ceb927abed9">
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