Description:

ELKI: "Environment for Developing KDD-Applications Supported by Index-Structures" is a development framework for data mining algorithms written in Java. It includes a large variety of popular data mining algorithms, distance functions and index structures.

Its focus is particularly on clustering and outlier detection methods, in contrast to many other data mining toolkits that focus on classification. Additionally, it includes support for index structures to improve algorithm performance such as R*-Tree and M-Tree.

The modular architecture is meant to allow adding custom components such as distance functions or algorithms, while being able to reuse the other parts for evaluation.

This package also includes the source code, since this software is meant for the rapid development of such algorithms, not so much for end users.

Changes to previous version:

Additions and Improvements from ELKI 0.5.5:

Algorithms

Clustering:

• Hierarchical Clustering - the slower naive variants were added, and the code was refactored
• Partition extraction from hierarchical clusterings - different linkage strategies (e.g. Ward)
• Canopy pre-Clustering
• Naive Mean-Shift Clustering
• Affinity propagation clustering (both with distances and similarities / kernel functions)
• K-means variations: Best-of-multiple-runs, bisecting k-means
• New k-means initialization: farthest points, sample initialization
• Cheng and Church Biclustering
• P3C Subspace Clustering
• One-dimensional clustering algorithm based on kernel density estimation

Outlier detection

• COP - correlation outlier probabilities
• LDF - a kernel density based LOF variant
• Simplified LOF - a simpler version of LOF (not using reachability distance)
• Simple Kernel Density LOF - a simple LOF using kernel density (more consistent than LDF)
• Simple outlier ensemble algorithm
• PINN - projection indexed nearest neighbors, via projected indexes.
• ODIN - kNN graph based outlier detection
• DWOF - Dynamic-Window Outlier Factor (contributed by Omar Yousry)
• ABOD refactored, into ABOD, FastABOD and LBABOD

Distances

• Geodetic distances now support different world models (WGS84 etc.) and are subtantially faster.
• Levenshtein distances for processing strings, e.g. for analyzing phonemes (contributed code, see "Word segmentation through cross-lingual word-to-phoneme alignment", SLT2013, Stahlberg et al.)
• Bray-Curtis, Clark, Kulczynski1 and Lorentzian distances with R-tree indexing support
• Histogram matching distances
• Probabilistic divergence distances (Jeffrey, Jensen-Shannon, Chi2, Kullback-Leibler)
• Kulczynski2 similarity
• Kernel similarity code has been refactored, and additional kernel functions have been added

Database Layer and Data Types

Projection layer * Parser for simple textual data (for use with Levenshtein distance) Various random projection families (including Feature Bagging, Achlioptas, and p-stable) Latitude+Longitude to ECEF Sparse vector improvements and bug fixes New filter: remove NaN values and missing values New filter: add histogram-based jitter New filter: normalize using statistical distributions New filter: robust standardization using Median and MAD New filter: Linear discriminant analysis (LDA)

Index Layer

• Another speed up in R-trees
• Refactoring of M- and R-trees: Support for different strategies in M-tree New strategies for M-tree splits Speedups in M-tree
• New index structure: in-memory k-d-tree
• New index structure: in-memory Locality Sensitive Hashing (LSH)
• New index structure: approximate projected indexes, such as PINN
• Index support for geodetic data - (Details: Geodetic Distance Queries on R-Trees for Indexing Geographic Data, SSTD13)
• Sampled k nearest neighbors: reference KDD13 "Subsampling for Efficient and Effective Unsupervised Outlier Detection Ensembles"
• Cached (precomputed) k-nearest neighbors to share across multiple runs
• Benchmarking "algorithms" for indexes

Mathematics and Statistics

• Many new distributions have been added, now 28 different distributions are supported
• Additional estimation methods (using advanced statistics such as L-Moments), now 44 estimators are available
• Trimming and Winsorizing
• Automatic best-fit distribution estimation
• Preprocessor using these distributions for rescaling data sets
• API changes related to the new distributions support
• More kernel density functions
• RANSAC covariance matrix builder (unfortunately rather slow)

Visualization

• 3D projected coordinates (Details: Interactive Data Mining with 3D-Parallel-Coordinate-Trees, SIGMOD2013)
• Convex hulls now also include nested hierarchical clusters

Other

• Parser speedups
• Sparse vector bug fixes and improvements
• Various bug fixes
• PCA, MDS and LDA filters
• Text output was slightly improved (but still needs to be redesigned from scratch - please contribute!)
• Refactoring of hierarchy classes
• New heap classes and infrastructure enhancements
• Classes can have aliases, e.g. "l2" for euclidean distance.
• Some error messages were made more informative.
• Benchmarking classes, also for approximate nearest neighbor search.

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Platform Independent

Data Formats: Arff, Other, Csv, Parser Extension Api

Tags: Clustering, Visualization, Algorithms, Evaluation, Anomaly Detection, Outlier Detection, Index Structures

Archive: download here

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