Clustering datasets

Speech and Image Processing Unit
School of Computing
University of Eastern Finland
P.O.Box 111
FIN-80101 Joensuu
Finland

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
UCI datasets
Thyroid 215 vectors, 2 clusters 5 dimensions		Thyroid dataset. Thyroid: ts txt
Wine 178 vectors, 3 clusters 13 dimensions		Wine dataset. Wine: ts txt
Yeast 1484 vectors, 10 clusters 8 dimensions		Yeast dataset. Yeast: txt Yeast_times100: ts txt
Breast 699 vectors, 2 clusters 9 dimensions		Breast-cancer-Wisconsin dataset. Breast: ts txt
Iris 150 vectors, 4 dimensions 3 clusters		Iris dataset. Iris: ts txt without labels txt with labels
Glass 214 vectors, 9 dimensions 7 clusters		Glass dataset. Glass: ts txt without labels txt with labels
Wdbc 569 vectors, 32 dimensions 2 clusters		Wdbc dataset. Wdbc: ts txt numeric, 31 dim. 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)
Shape sets

		Third column is the label.
Aggregation 788 vectors, 2 dimensions 7 clusters		Aggregation: txt Gionis, A., H. Mannila, and P. Tsaparas, Clustering aggregation. ACM Transactions on Knowledge Discovery from Data (TKDD), 2007. 1(1): p. 1-30.
Compound 399 vectors, 2 dimensions 6 clusters		Compound: txt Zahn, C.T., Graph-theoretical methods for detecting and describing gestalt clusters. Computers, IEEE Transactions on, 1971. 100(1): p. 68-86.
Pathbased 300 vectors, 2 dimensions 3 clusters		Pathbased: txt Chang, H. and D.Y. Yeung, Robust path-based spectral clustering. Pattern Recognition, 2008. 41(1): p. 191-203.
Spiral 312 vectors, 2 dimensions 3 clusters		Spiral: txt Chang, H. and D.Y. Yeung, Robust path-based spectral clustering. Pattern Recognition, 2008. 41(1): p. 191-203.
D31 3100 vectors, 2 dimensions 31 clusters		D31: txt Veenman, C.J., M.J.T. Reinders, and E. Backer, A maximum variance cluster algorithm. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2002. 24(9): p. 1273-1280.
R15 600 vectors, 2 dimensions 15 clusters		R15: txt Veenman, C.J., M.J.T. Reinders, and E. Backer, A maximum variance cluster algorithm. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2002. 24(9): p. 1273-1280.
Jain 373 vectors, 2 dimensions 2 clusters		Jain: txt Jain, A. and M. Law, Data clustering: A user's dilemma. Lecture Notes in Computer Science, 2005. 3776: p. 1-10.
Flame 240 vectors, 2 dimensions 2 clusters		Flame: txt Fu, L. and E. Medico, FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data. BMC bioinformatics, 2007. 8(1): p. 3.
Mopsi locations
Users' locations 13467 vectors, 2 dimensions		Mopsi locations Finland until 2012 dataset. Users' locations: cb txt
Users' locations, Joensuu 6014 vectors, 2 dimensions		Users' locations in Joensuu 2012 dataset. Users' locations Joensuu: ts txt
Europe
Europe 169308 vectors, 2 dimensions		Europe dataset. Europe: ts txt

Clustering datasets

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