Projects authored by database systems group university of munich.
ELKI is a framework for implementing data-mining algorithms with support for index structures, that includes a wide variety of clustering and outlier detection methods.
This is mostly a bug fix release. A lot of small issues have been
fixed that improve performance, make error reporting a lot better,
ease the use of sparse vectors and external precomputed distances, for
This will be the last ELKI release to support Java 6. The next ELKI
release will require Java 7.
Some new LOF variants (LDF, SimpleLOF, SimpleKernelDensityLOF)
Correlation Outlier Probabilities (ICDM 2012)
A naive mean-shift clustering
Single-link clustering (SLINK algorithm) should be significantly
faster due to optimized data structures
"Benchmarking" algorithms for measuring the performance of index structures
Bulk loading R-Trees should be faster - in particular Sort Tile
Recursive can work very well.
M-Trees have been refactored and optimized for double distances
Bundle format (work in progress): low-level binary format for fast
DBID and DataStore layer received some additional classes for
further performance improvements
KNN heap structures were revisited. The code is less clean now, but
performs better in benchmarks.
General clean up and API simplifications
Some additional modules and improvements
There is a new parameter class, RandomParameter
Some new distributions were added, also to the data set generator.
The website has new tutorials, including one on a k-means variation
that produces equal sized clusters.