https://github.com/deric/clustering-benchmark
https://github.com/deric/clustering-benchmark
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/deric/clustering-benchmark
- Owner: deric
- Created: 2015-06-09T18:57:12.000Z (about 11 years ago)
- Default Branch: master
- Last Pushed: 2019-06-17T13:32:31.000Z (almost 7 years ago)
- Last Synced: 2025-03-30T05:11:21.767Z (about 1 year ago)
- Language: Java
- Size: 23.3 MB
- Stars: 167
- Watchers: 4
- Forks: 119
- Open Issues: 0
-
Metadata Files:
- Readme: README-old.asc
Awesome Lists containing this project
README
# Clustering datasets
## Datasets
This project contains collection of labeled clustering problems that can be found in the literature. Most of datasets were artificially created.
All datasets can be found link:https://github.com/deric/clustering-benchmark/tree/master/src/main/resources/datasets/artificial[data folder].
### 2d-10c
[align="right",options="header"]
|===
| data points | clusters | dimension
| 2990 | 10 | 2
|===
image::https://github.com/deric/clustering-benchmark/blob/images/fig/artificial/2d-10c.png["2d-10c",400,float="left"]
[.float .right]
* link:https://github.com/deric/clustering-benchmark/blob/master/src/main/resources/datasets/artificial/2d-10c.arff[ARFF]
* link:https://github.com/deric/handl-data-generators[generator]
> J. Handl and J. Knowles, “Multiobjective clustering with automatic
> determination of the number of clusters,” UMIST, Tech. Rep., 2004.
### atom
[align="right",options="header"]
|===
| data points | clusters | dimension
| 800 | 2 | 3
|===
image::https://github.com/deric/clustering-benchmark/blob/images/fig/artificial/atom.png["atom",400,float="left"]
[.float .right]
* source: link:https://www.uni-marburg.de/fb12/datenbionik/data?language_sync=1[FCPS]
* link:https://github.com/deric/clustering-benchmark/blob/master/src/main/resources/datasets/artificial/atom.arff[ARFF]
### aggregation
[align="right",options="header"]
|===
| data points | clusters | dimension
| 788 | 7 | 2
|===
image::https://github.com/deric/clustering-benchmark/blob/images/fig/artificial/aggregation.png[aggregation,400,float="left"]
[.float .right]
* link:https://github.com/deric/clustering-benchmark/blob/master/src/main/resources/datasets/artificial/aggregation.arff[ARFF]
* link:http://cs.joensuu.fi/sipu/datasets/[original source]
> Gionis, A., H. Mannila, and P. Tsaparas, Clustering aggregation.
> ACM Transactions on Knowledge Discovery from Data (TKDD), 2007. 1(1): p. 1-30.
### chainlink
[align="right",options="header"]
|===
| data points | clusters | dimension
| 1000 | 2 | 3
|===
image::https://github.com/deric/clustering-benchmark/blob/images/fig/artificial/chainlink.png["chainlink",400,float="left"]
[.float .right]
* source: link:https://www.uni-marburg.de/fb12/datenbionik/data?language_sync=1[FCPS]
* link:https://github.com/deric/clustering-benchmark/blob/master/src/main/resources/datasets/artificial/chainlink.arff[ARFF]
> Alfred Ultsch, Clustering with SOM: U*C,
> in Proc. Workshop on Self Organizing Feature Maps ,pp 31-37 Paris 2005.
### D31
[align="right",style="asciidoc",options="noborders,wide"]
|===
| data points | 3100
| clusters | 31
| dimensions | 2
| image::https://github.com/deric/clustering-benchmark/blob/images/fig/artificial/D31.png["D31",400,float="left"] | * link:https://github.com/deric/clustering-benchmark/blob/master/src/main/resources/datasets/artificial/D31.arff[ARFF]
|===
> Veenman, C.J., M.J.T. Reinders, and E. Backer,
> A maximum variance cluster algorithm. IEEE Trans. Pattern Analysis and Machine Intelligence 2002. 24(9): p. 1273-1280.
### 3MC
[align="right",options="header",style="literal"]
|===
| data points | clusters | dimension
| 400 | 3 | 2
|===
[.float .right]
image::https://github.com/deric/clustering-benchmark/blob/images/fig/artificial/3MC.png["3MC",400,float="left"]
### DS577
[align="right",options="header"]
|===
| data points | clusters | dimension
| 577 | 3 | 2
|===
image::https://github.com/deric/clustering-benchmark/blob/images/fig/artificial/DS577.png["D31",400,float="left"]
[.float .right]
* link:https://github.com/deric/clustering-benchmark/blob/master/src/main/resources/datasets/artificial/DS577.arff[ARFF]
> M. C. Su, C. H. Chou, and C. C. Hsieh, “Fuzzy C-Means Algorithm with a Point Symmetry Distance,”
> International Journal of Fuzzy Systems, vol. 7, no. 4, pp. 175-181, 2005.
### cluto-t4_8k
[align="right",options="header"]
|===
| data points | clusters | dimension
| 8000 | 7 | 2
|===
image::https://github.com/deric/clustering-benchmark/blob/images/fig/artificial/cluto-t4_8k.png["cluto-t4_8k",400,float="left"]
[.float .right]
* link:https://github.com/deric/clustering-benchmark/blob/master/src/main/resources/datasets/artificial/cluto-t4.8k.arff[ARFF]
> G. Karypis, “CLUTO A Clustering Toolkit,”
> Dept. of Computer Science, University of Minnesota, Tech. Rep. 02-017, 2002, available at
http://www.cs.umn.edu/ ̃cluto.
## Experiments
This project contains set of clustering methods benchmarks on various dataset. The project is dependent on [Clueminer project](https://github.com/deric/clueminer).
in order to run benchmark compile dependencies into a single JAR file:
mvn assembly:assembly
# Consensus experiment
allows running repeated runs of the same algorithm:
```
./run consensus --dataset "triangle1" --repeat 10
```
by default k-means algorithm is used.
For available datasets see [resources folder](https://github.com/deric/clustering-benchmark/tree/master/src/main/resources/datasets/artificial).