https://github.com/rcv911/dendrogram
How to draw Dendrogram in clustering analysis
https://github.com/rcv911/dendrogram
cluster cluster-analysis clustering clustering-algorithm clustering-methods dendrogram
Last synced: 5 days ago
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How to draw Dendrogram in clustering analysis
- Host: GitHub
- URL: https://github.com/rcv911/dendrogram
- Owner: rcv911
- Created: 2017-12-21T15:27:03.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2017-12-23T18:36:43.000Z (about 8 years ago)
- Last Synced: 2023-11-29T20:43:10.608Z (about 2 years ago)
- Topics: cluster, cluster-analysis, clustering, clustering-algorithm, clustering-methods, dendrogram
- Language: Python
- Homepage:
- Size: 151 KB
- Stars: 1
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Dendrogram. How to draw.
## Description
It's one of the clustering methods using [hierarchical clustering](https://en.wikipedia.org/wiki/Hierarchical_clustering).
We are going to use [special Scipy library](https://docs.scipy.org/doc/scipy/reference/cluster.hierarchy.html) for Python where you can find
useful function for clustering analysis saving your time.
This special Scipy library in [GitHub.](https://github.com/scipy/scipy/blob/master/scipy/cluster/hierarchy.py)
>We are going to use this 2 data from this [project](https://github.com/rcv911/Cluster_generation) but changing some parameters:
## Algorithm
+ So, we have distance matrix
```python
d = sch.distance.pdist(X) # import scipy.cluster.hierarchy as sch
```
or manually (using Euclidean distance)
```python
for i in range(N):
for j in range(i+1, N):
d[j, i] = d[i, j] = (sum((X[i, :]-X[j, :])**2))**0.5
```
> It's important. You can choose any of the metrics in Python function
[scipy.cluster.hierarchy.distance.pdist()](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.pdist.html)
+ We know the distances between each pair of points. We assume each point is a cluster and we starts to combining them.
> Important. We combine **only two** of the cluster at each step. **Not the points**. One cluster shifts as a whole to another cluster.
+ 2 stopping criteria:
+ you achieved critical distance.
+ you have the right number of clusters
## Results
> dendrogram for the first test data with 1 cluster
> dendrogram for the second test data with 3 clusters
## Learn more
- [Wiki](https://en.wikipedia.org/wiki/Dendrogram)
- [Wiki2](https://wiki2.org/en/Dendrogram)
- [Hierarchical Clustering/Dendrograms](https://www.ncss.com/wp-content/themes/ncss/pdf/Procedures/NCSS/Hierarchical_Clustering-Dendrograms.pdf)
## Installation
You can use [Python](https://www.python.org/) with data package: [Anaconda](https://www.anaconda.com/) or [Miniconda](https://conda.io/miniconda).
There's another way - use [Portable Python](http://portablepython.com/). Also you can use whatever IDE for Python.
## License
Free