Download http://kdd.ics.uci.edu

The UC Irvine Knowledge Discovery in Databases (KDD) Archive is a new online
repository of large data sets which encompasses a wide variety of data types,
analysis tasks, and application areas. The primary role of this repository is to
enable researchers in knowledge discovery and data mining to scale existing and
future data analysis algorithms to very large and complex data sets. An
important goal of the archive is to foster interdisciplinary research between
computer scientists, statisticians and mathematicians on analysis algorithms for
massive data sets.
This archive is supported by the Information and Data Management Program at
the National Science Foundation, and is intended to expand the current UCI
Machine Learning Database Repository to data sets that are orders of magnitude
larger and more complex.
We are seeking submissions of large, well-documented data sets that can be made
publicly available. Data types and tasks of interest include, but is not limited to:
Data Types
Tasks
multivariate
time series
sequential
relational
text
image
spatial
multimedia
transactional
heterogeneous
sound/audio
classification
regression
clustering
density estimation
retrieval
causal modeling
visualization
collaborative filtering
exploratory data analysis
data cleaning
recommendation systems
Submission Guidelines: Please see the UCI KDD Archive web site for detailed
instructions.
http://kdd.ics.uci.edu