ELKI 0.7.0 release notes

  • ELKI is now available on Maven.
    <!-- ELKI core, without visualization -->
    <!-- You only need this dependency if you need visualization -->
    Please clone for a minimal project example.
  • Uncertain data types, and clustering algorithms for uncertain data.
  • Major refactoring of distances - removal of Distance values and removed support for non-double-valued distance functions (in particular DoubleDistance was removed). While this reduces the generality of ELKI, we could remove about 2.5% of the codebase by not having to have optimized codepaths for double-distance anymore. Generics for distances were present in almost any distance-based algorithm, and we were also happy to reduce the use of generics this way. Support for non-double-valued distances can trivially be added again, e.g. by adding the specialization one level higher: at the query instead of the distance level, for example.
  • In this process, we also removed the Generics from NumberVector. The object-based get was deprecated for a good reason long ago, and e.g. doubleValue are more efficient (even for non-DoubleVectors).
  • Dropped some long-deprecated classes.
  • K-means:
    • speedups for some initialization heuristics.
    • K-means++ initialization no longer squares distances (again).
    • farthest-point heuristics now uses minimum instead of sum (renamed).
    • additional evaluation criteria.
    • Elkan's and Hamerly's faster k-means variants.
  • CLARA clustering.
  • X-means.
  • Hierarchical clustering:
    • Renamed naive algorithm to AGNES.
    • Anderbergs algorithm (faster than AGNES, slower than SLINK).
    • CLINK for complete linkage clustering in O(n²) time, O(n) memory.
    • Simple extraction from HDBSCAN.
    • "Optimal" extraction from HDBSCAN.
    • HDBSCAN, in two variants.
  • LSDBC clustering.
  • EM clustering was refactored and moved into its own package. The new version is much more extensible.
  • OPTICS clustering:
    • Added a list-based variant of OPTICS to our heap-based.
    • FastOPTICS (contributed by Johannes Schneider).
    • Improved OPTICS Xi cluster extraction.
  • Outlier detection:
    • KDEOS outlier detection (SDM14).
    • k-means based outlier detection (distance to centroid) and Silhouette coefficient based approach (which does not work too well on the toy data sets - the lowest silhouette are usually where two clusters touch).
    • bug fix in kNN weight, when distances are tied and kNN yields more than k results.
    • kNN and kNN weight outlier have their k parameter changed: old 2NN outlier is now 1NN outlier, as commonly understood in classification literature (1 nearest neighbor other than the query object; whereas in database literature the 1NN is usually the query object itself). You can get the old result back by decreasing k by one easily.
    • LOCI implementation is now only O(n3 log n) instead of O(n4).
    • Local Isolation Coefficient (LIC).
    • IDOS outlier detection with intrinsic dimensionality.
    • Baseline intrinsic dimensionality outlier detection.
    • Variance-of-Volumes outlier detection (VOV).
  • Parallel computation framework, and some parallelized algorithms
    • Parallel k-means.
    • Parallel LOF and variants.
  • LibSVM format parser.
  • kNN classification (with index acceleration).
  • Internal cluster evaluation:
    • Silhouette index.
    • Simplified Silhouette index (faster).
    • Davis-Bouldin index.
    • PBM index.
    • Variance-Ratio-Criteria.
    • Sum of squared errors.
    • C-Index.
    • Concordant pair indexes (Gamma, Tau).
    • Different noise handling strategies for internal indexes.
  • Statistical dependence measures:
    • Distance correlation dCor.
    • Hoeffings D.
    • Some divergence / mutual information measures.
  • Distance functions:
    • Big refactoring.
    • Time series distances refactored, allow variable length series now.
    • Hellinger distance and kernel function.
  • Preprocessing:
    • Faster MDS implementation using power iterations.
  • Indexing improvements:
    • Precomputed distance matrix "index".
    • iDistance index (static only).
    • Inverted-list index for sparse data and cosine/arccosine distance.
    • Cover tree index (static only).
    • Additional LSH hash functions.
  • Frequent Itemset Mining:
    • Improved APRIORI implementation.
    • FP-Growth added.
    • Eclat (basic version only) added.
  • Uncertain clustering:
    • Discrete and continuous data models.
    • FDBSCAN clustering.
    • UKMeans clustering.
    • CKMeans clustering.
    • Representative Uncertain Clustering (Meta-algorithm).
    • Center-of-mass meta Clustering (allows using other clustering algorithms on uncertain objects).
  • Mathematics:
    • Several estimators for intrinsic dimensionality.
  • MiniGUI has two "secret" new options: -minigui.last -minigui.autorun to load the last saved configuration and run it, for convenience.
  • Logging API has been extended, to make logging more convenient in a number of places (saving some lines for progress logging and timing).
Last modified 4 years ago Last modified on Nov 27, 2015, 6:25:56 PM