https://github.com/rcv911/nearest-neighbor-search
Nearest neighbor search in two dimension
https://github.com/rcv911/nearest-neighbor-search
cluster cluster-analysis clustering clustering-algorithm nearest-neighbor nearest-neighbor-search nearest-neighbors
Last synced: 5 months ago
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Nearest neighbor search in two dimension
- Host: GitHub
- URL: https://github.com/rcv911/nearest-neighbor-search
- Owner: rcv911
- Created: 2017-12-20T14:02:31.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2017-12-20T15:39:07.000Z (over 8 years ago)
- Last Synced: 2023-11-29T20:43:08.069Z (over 2 years ago)
- Topics: cluster, cluster-analysis, clustering, clustering-algorithm, nearest-neighbor, nearest-neighbor-search, nearest-neighbors
- Language: Python
- Homepage:
- Size: 292 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Nearest Neighbor Search
## Description
A baseline method for clusterings.
>We are going to use this 2 data from this [project](https://github.com/rcv911/Cluster_generation):
## Algorithm
+ User should determine the critical distance (manually)
```python
dc =
```
+ Using [Euclidean distance](https://en.wikipedia.org/wiki/Euclidean_distance) calculate matrix distance.
Note, you can use another distance function like [Manhattan distance](https://en.wikipedia.org/wiki/Taxicab_geometry).
```python
for i in range(N):
for j in range(i+1, N):
M[j, i] = M[i, j] = (sum((x[i, :]-x[j, :])**2))**0.5
```
+ Find 'embryo' -> the most closest dots
+ Find indexes - the minimum distance among all
+ 3 lists:
+ list 1 - long added points and their neighbors
+ list 2 - fresh points added in the last step. It needs find their neighbors
+ list 3 - this points is a new neighbors from list 2
Now, how it works with lists:
+ 1st step:
+ list 1 is empty
+ list 2 has 'embryo'
+ list 3 new neighbors
+ 2nd step:
+ list 2 is list 1 now
+ list 3 is list 2 now
+ list 3 is empty
+ etc.
## Results
> if dc = 5 we have 3 clusters
> if dc = 6 we have 2 clusters
> if dc = 7 we have 1 cluster
> if dc = 1.5 we have 4 clusters
> if dc = 2 we have 3 clusters
> if dc = 5 we have 2 clusters
## Learn more
- Source 1: [Wiki](https://en.wikipedia.org/wiki/Nearest_neighbor_search)
- Source 2: [Wiki2](https://wiki2.org/en/Nearest_neighbor_search)
## Projects
You can find another ready projects for analys like [QGIS](http://www.qgistutorials.com/en/docs/nearest_neighbor_analysis.html)
## 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