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