Clustering datasets

Image data
Bridge (256x256)	4096 vectors, 16-d	4x4 pixel blocks  ts  txt 4x4 binarized pixel blocks  ts  txt 4x4 pixel blocks: 25% randomly sampled (for training)  ts  txt 4x4 pixel blocks: 75% randomly sampled (for testing)  ts  txt
House (256x256)	34112 vectors, 3-d	RGB-values, quantized to 5 bits per color  ts  txt RGB-values, 8 bits per color  ts  txt
Miss America (360x288)	6480 vectors, 16-d	4x4 pixel blocks from the difference image of frame 1 and 2  ts  txt 4x4 pixel blocks from the difference image of frame 2 and 3  ts  txt
Birch-sets
Birch1	Birch2	Synthetic 2-d data with 100 000 vectors and 100 clusters. Zhang et al., "BIRCH: A new data clustering algorithm and its applications", Data Mining and Knowledge Discovery, 1 (2), 141-182, 1997.
Birch3		Birch1: Clusters in regular grid structure  ts  txt Birch2: Clusters at a sine curve  ts  txt Birch3: Random sized clusters in random locations  ts  txt
S-sets
S1 S3	S2 S4	Synthetic 2-d data with 5000 vectors and 15 Gaussian clusters with different degree of cluster overlapping. P. Fränti and O. Virmajoki, "Iterative shrinking method for clustering problems", Pattern Recognition, 39 (5), 761-765, May 2006. S1:  ts  txt S2:  ts  txt S3:  ts  txt S4:  ts  txt Source and labels:  zip
A-sets
A1 3000 vectors, 20 clusters	A2 5250 vectors, 35 clusters	Synthetic 2-d data with varying number of clusters and vectors. A1:  ts  txt A2:  ts  txt A3:  ts  txt
A3 7500 vectors, 50 clusters
Dim-sets
	Dim2	Synthetic data with Gaussian clusters in multi-dimensional space. 1351-10126 vectors, 2-d - 15-d ts  txt
DIM-sets (other)
DIM032 1024 vectors, 16 clusters 32 dimensions	DIM064 1024 vectors, 16 clusters 64 dimensions	Dim-sets. DIM032:  ts  txt DIM064:  ts  txt DIM128:  ts  txt DIM256:  ts  txt DIM512:  ts  txt DIM1024:  ts  txt
DIM128 1024 vectors, 16 clusters 128 dimensions	DIM256 1024 vectors, 16 clusters 256 dimensions
DIM512 1024 vectors, 16 clusters 512 dimensions	DIM1024 1024 vectors, 16 clusters 1024 dimensions
KDDCUP04Bio set
KDDCUP04Bio 145751 vectors, 2000 clusters 74 dimensions		KDDCUP04Bio biology dataset. KDDCUP04Bio:  ts  txt
Thyroid set
Thyroid 215 vectors, 2 clusters 5 dimensions		Thyroid dataset. Thyroid:  ts  txt
Wine set
Wine 178 vectors, 3 clusters 13 dimensions		Wine dataset. Wine:  ts  txt
Yeast set
Yeast 1484 vectors, 10 clusters 8 dimensions		Yeast dataset. Yeast:  txt Yeast_times100:  ts  txt
Breast-cancer-Wisconsin set
Breast 699 vectors, 2 clusters 9 dimensions		Breast-cancer-Wisconsin dataset. Breast:  ts  txt
g2 sets
g2-2-30 1024 vectors per cluster, 2 clusters 1-1024 dimensions variance 10-100		Gaussian clusters dataset. g2:  ts's in zip file (53MB)

Related links

• Programming interface (modu*.zip) to handle data sets (cb/ts-format)
• Windows software for visualizing data sets
• Software for converting data sets to text
• PPM/PNM/PBM image formats