ELKI 0.7.5 release notes
ELKI 0.7.5 “Heidelberg” is available on Maven and on our releases web page:
Gradle:
Maven:
Please clone https://github.com/elkiproject/exampleelkiproject for a minimal project example.
Upcoming major changes
The next ELKI release will shorten all package names. We will also change the group ID to reflect that the project moved to https://elkiproject.github.io/.
Since we will rename all packages, we will also use this opportunity to simplify other class names, such as “DistanceFunction” to “Distance”.
Futher breaking changes include changes to the result hierarchy and metadata management. These are necessary for important new functionality (such as automatic indexing, and garbage collection).
For ELKI 0.8.0, we will likely target Java 11 or 12,
so 0.7.5 is supposedly the last version to support Java 8.
We may begin using the var
feature of Java 9 in cases where it
makes the code more readable.
Thus the next 0.8.0 release will not be backwards compatible at all.
New functionality
Clustering
 Gaussian Mixture Modeling Improvements:
 Additional models
 Improved numerical stability
 MAP for robustness
 Better initialization
 DBSCAN Improvements:
 GDBSCAN with similarity functions
 Parallel Generalized DBSCAN related to
 Hierarchical clustering additions:
 NNChain algorithm
 MiniMax clustering
 Flexible Beta Linkage
 Minimum Variance Linkage
 Cluster extraction from dendrograms with handling of noise points and minimum cluster size

Basic BIRCH: clustering into leaves, with support for:
Distances:
 Average Intercluster Distance
 Average Intracluster Distance
 Centroid Euclidean Distance
 Centroid Manhattan Distance
 Variance Increase Distance
Merge Criterions:
 Diameter Criterion
 Euclidean Distance Criterion
 Radius Criterion
 PAM clustering additions:
 Reynolds’ PAM and FastPAM
 Improvements to CLARA and FastCLARA
 CLARANS and FastCLARANS
 kMeans clustering additions:
 Annulus algorithm
 Exponion algorithm
 Simplified Elkan’s algorithm
 kMeans– (more robust to noise)
 kMeans and PAM initialization additions:
 Linear Approximative BUILD (LAB)
 Ostrovsky’s initial means
 Park’s initial medoids
 Generated from a normal distribution
 Leader Clustering
 FastDOC subspace clustering
Association Rule Mining
 Association Rule Generation
 Interestingness Measures:
 Added Value
 Certainty Factor
 Confidence
 Conviction
 Cosine
 Gini Index
 J Measure
 Jaccard
 Klosgen
 Leverage
 Lift
Outlier Detection
 ClusterBased Local Outlier Factor (CBLOF)
 KNN Data Descriptor (KNNDD)
 Stochastic Outlier Selection (SOS), with kNN approximation (KNNSOS), and intrinsic dimensionality (ISOS)
Projections and Embeddings
 Stochastic Neighbor Embedding (SNE)
 tStochastic Neighbor Embedding (tSNE)
 Barnes Hut approximation for tSNE
 Intrinsic tSNE
Change Point Detection in Time Series
 Offline Change Point Detection
 SigniTrendbased Change Detection
Distance and Similarity Functions
 Cosine distances optimized for unitlength vectors
 Mahalanobis
 Chi Distance
 Fisher Rao Distance
 Triangular Discrimination
 Triangular Distance
Evaluation
 DensityBased Cluster Validation (DBCV)
 Discounted Cumulative Gain (DCG)
 Normalized Discounted Cumulative Gain (NDCG)
Indexing Additions
 Basic index support for similarities
 NN Descent
 MTree enhancements:
 Farthest Points Split
 MST Split
 Balanced Distribution
 Farthest Balanced Distribution
 Generalized Hyperplane Distribution
 XTree (nonreviewed code)
Statistics Layer
 ExpGamma distribution parameters using the method of moments
 LogGamma distribution parameters using the method of moments
 ALID estimator of intrinsic dimensionality
Other Improvements
 ELKI Builder API (see Section 5)
 Integer range parameters (e.g.,
1,2,..,10,20,..,100,200,..,1000
)  Xoroshiro 128 fast random generator
 Dendrogram visualization for hierarchical clusterings
 Numerous unit tests
 Many bug fixes found in testing and reported by users
See also release notes 0.7 and release notes 0.7.1 for additional release notes of ELKI 0.7.0 and 0.7.1