About: 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 example.

This will be the last ELKI release to support Java 6. The next ELKI release will require Java 7.

Algorithms

• 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

Index layer

• 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

Database layer

• Bundle format (work in progress): low-level binary format for fast data exchange
• 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.

Visualizations

• General clean up and API simplifications
• Some additional modules and improvements

Various

• There is a new parameter class, RandomParameter
• Some new distributions were added, also to the data set generator.

Tutorials

• The website has new tutorials, including one on a k-means variation that produces equal sized clusters.

About: An implementation of MROGH descriptor. For more information, please refer to: “Bin Fan, Fuchao Wu and Zhanyi Hu, Aggregating Gradient Distributions into Intensity Orders: A Novel Local Image Descriptor, CVPR 2011, pp.2377-2384.” The most up-to-date information can be found at : http://vision.ia.ac.cn/Students/bfan/index.htm

Initial Announcement on mloss.org.

About: Software to perform isoline retrieval, retrieve isolines of an atmospheric parameter from a nadir-looking satellite.

Added screenshot, keywords

About: Grid-Soccer Simulator is a multi-agent soccer simulator in a grid-world environment. The environment provides a test-bed for machine-learning, and control algorithms, especially multi-agent reinforcement learning.

Initial Announcement on mloss.org.

About: BACKGROUND:Over the last decade several prediction methods have been developed for determining the structural and functional properties of individual protein residues using sequence and sequence-derived information. Most of these methods are based on support vector machines as they provide accurate and generalizable prediction models. RESULTS:We present a general purpose protein residue annotation toolkit (svmPRAT) to allow biologists to formulate residue-wise prediction problems. svmPRAT formulates the annotation problem as a classification or regression problem using support vector machines. One of the key features of svmPRAT is its ease of use in incorporating any user-provided information in the form of feature matrices. For every residue svmPRAT captures local information around the reside to create fixed length feature vectors. svmPRAT implements accurate and fast kernel functions, and also introduces a flexible window-based encoding scheme that accurately captures signals and pattern for training eective predictive models. CONCLUSIONS:In this work we evaluate svmPRAT on several classification and regression problems including disorder prediction, residue-wise contact order estimation, DNA-binding site prediction, and local structure alphabet prediction. svmPRAT has also been used for the development of state-of-the-art transmembrane helix prediction method called TOPTMH, and secondary structure prediction method called YASSPP. This toolkit developed provides practitioners an efficient and easy-to-use tool for a wide variety of annotation problems. Availability: http://www.cs.gmu.edu/~mlbio/svmprat/

Initial Announcement on mloss.org.