In ELKI, data mining algorithms and data management tasks are separated and allow for an independent evaluation. This separation makes ELKI unique among data mining frameworks like Weka or YALE and frameworks for index structures like GiST.
At the same time, ELKI is open to arbitrary data types, distance or similarity measures, or file formats.
The fundamental approach is the independence of file parsers or database connections, data types, distances, distance functions, and data mining algorithms. Helper classes, e.g. for algebraic or analytic computations are available for all algorithms on equal terms.

ELKI is the acronym for “ELktonichen KIchStruktur”, a German acronym for “The numerical data structure kit”. ELKI is a free open source software for knowledge discovery in data.
ELKI is written in Java and consists of six main components.

Comments and improvements (additions, corrections) welcome

IFile:
The input file structures for ELKI are the files obtained from the input streams of file parsers (DBF, xls, sas, text, native files,…) or obtained with the ExternalMemory class.
Besides these data types, it is also possible to read compressed files like zip and tar archives.

Types:
The standard types of the ELKI framework are the file parsers and databases. After reading a file, data mining algorithms can be applied to it or it can be put into a database.
The file parser component is used for writing: the database can be loaded from disk in read-only mode or it can be used as an object to be saved. Both parser and database can store null values.

Filtering:
The filters are a special class from ELKI. Filters are independent from data mining algorithms and can be used to select the input data in a way like a logical “and” or “or”.

Distance or Similarity Measures:
The distance or similarity measures are calculated for all pairs of objects using predefined functions like Euclidean, Manhattan or Minkowski distance or the Jaccard similarity for set similarities.

Descriptors:
The descriptors are global numeric values of data points. A descriptor can be used to describe a data point based on its other data points.

Algorithms:
The algorithms are a special class from ELKI and correspond to the algorithms implemented by the algorithms framework for distance or similarity measures. A subclass of algorithm is the SM (search method) that allows for a comparison of several different search methods like hill climbing, local search or random search and that can also be instantiated with custom search techniques.

Constructors:
The constructors are a special class from ELKI and correspond to the constructors of classes for distance or similarity measures. The constructor allows for the construction of descriptors that depend on values of other objects.

There are two main reasons for which ELKI is of special interest.

ELKI is a free and open source project that is available under the GNU GPL (Version 2)

ELKI is an open source (ASF 2-Clause License) toolkit for scientific data analysis. It aims at being an all-rounder data mining framework with many capabilities and features.

ELKI Web Project (Free Software):
ELKI Web Project aims at being a portal to the ELKI project. ELKI Web Project provides the possibility to download and install ELKI and use all available features for free.
Installing ELKI is a four step process. First of all you need to download ELKI, unpack it and place the extracted files under your home directory. In a second step you need to set up your LDAP directory manager for accessing it. The third step is to configure the Eclipse IDE environment (or any other Eclipse compatible development environment). The last step is to run the WEBJAR (ELKI web server), which is included in the distribution of ELKI.
ELKI Documentation:

ELKI User Guide:
This user guide is a quick introduction to ELKI and what it can do. However, the user guide contains also a detailed list of all available features that can be used with ELKI.

ELKI Administration User Guide:
This user guide shows the setup of LDAP database, LDAP connection settings and other related settings.

ELKI User Guide:
This user guide is dedicated to a detailed explanation of all available ELKI features including all core algorithms.

ELKI Presentations:
In this folder you will find all presentation available for the ELKI user group, the developers’ conference and the Open Source Software Symposium 2008.

ELKI Overview Presentation:
This presentation is dedicated to a presentation of ELKI including more or less all available features.A method for mounting a semiconductor chip on a laminate substrate has been conventionally used when an electronic part is constructed. According to this conventional mounting method, electrode pads formed on a semiconductor chip are respectively connected to conductor pads formed on a laminate substrate. The laminate substrate is assembled into a semiconductor package. In order to mount the semiconductor chip on the laminate substrate, a molding resin is applied on a back surface of the semiconductor chip. The semiconductor chip and the laminate substrate are mounted on each other and the semiconductor chip and the laminate substrate are coupled to each other by the molding resin.
However, as the number of electrode pads formed on the semiconductor chip increases and the number of conductor pads formed on
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Elki is a java library for data mining and machine learning based on two concepts:

Unsupervised data mining is based on data clustering and the estimation of the underlying structure of the data. A good candidate structure can be expressed by a cluster graph. This cluster graph links data points with one another, forming cluster edges. Some clusters may be more central or of greater interest than others.

The second concept is supervised learning. For this purpose ELKI provides methods for various pattern recognition problems, e.g. classification or regression. It also offers a machine learning module,
which includes methods for classification and regression. ELKI includes multiple non-parametric algorithms for exploratory data analysis (EDA). For supervised machine learning there are kernel methods, which are suitable to support multi-class problems. Typical kernel methods are linear or multilinear. However, more complex kernels can also be implemented.
ELKI requires to define a distance function for measuring similarity between all data items. The distance can be either a distance function or a similarity measure. The latter is a classical concept in statistical theory. In ELKI data mining, distances or similarity measures can be based on various numbers such as text length, motif sizes, or the Hamming distance.
Besides the possibility to work with various forms of distance measures, ELKI includes support for similarity measures, e.g. for calculating cosine similarity.
ELKI has been developed as a Java library. ELKI has been tested successfully with Java SE 6 and Java SE 7. Some data types that are not supported by all data mining algorithms are implemented using the JNI library.
Features:

12/6/2011 @ 8:08PM

taken from the About page:
In ELKI, data mining algorithms and data management tasks are separated and allow for an independent evaluation. This separation makes ELKI unique among data mining frameworks like Weka or YALE and frameworks for index structures like GiST.
At the same time, ELKI is open to arbitrary data types, distance or similarity measures, or file formats.
The fundamental approach is the independence of file parsers or database connections, data types, distances, distance functions, and data mining algorithms. Helper classes, e.g. for algebraic or analytic computations are available for all algorithms on equal terms.
ELKI Description:
Elki is a java library for data mining and machine learning based on two concepts:

Unsupervised data mining is based on data

The ELKI project is a reference implementation of the ELKI Java Framework and the ELKI Java-Library. It is developed by Georg Fritzsche, with the collaboration of Martin Braun and Harald Puehring.
The ELKI framework consists of the ELKI Java Framework, the ELKI Java-Library, and the ELKI Core. All of these components are open-source and free software.
The ELKI Java Framework is used for compiling and testing of data mining algorithms. Its components are used by various Java data mining projects such as Apriori, jMiner, jTreg, machine learning libraries, and plug-ins for projects like Apache Mahout, Apache Hive, or Apache Lucene.
The ELKI Java-Library and the ELKI Core offer programming and data management functionality for various data types, file formats, algorithms, and distance and similarity functions.
ELKI Version 2.0 is released in the Java and R languages. The ELKI Java Framework is available under the Apache 2.0 license.
ELKI is a member of the ELKI consortium. The consortium consists of the ELKI project and all its members. The ELKI consortium aims to develop, distribute, and maintain a Java framework for data mining applications.
The ELKI project offers:
the ELKI Java Framework (core components for algorithms and file I/O)
the ELKI Java-Library (core components for data types, file formats, and distances/similarities)
the ELKI Core (support for all algorithms and distances/similarities)
The ELKI project is the leading Java data mining framework and open source data mining library, with over a billion downloads and use on more than 50 million users worldwide.
The project’s core components include data mining algorithms (all supported Java languages, e.g. Java, R and JavaScript), data structures, numerical functions, data type and distance functions.
ELKI Java Framework and ELKI Java-Library:
The ELKI Java Framework (EJF) is a modular Java data mining framework used by Java-based applications, e.g. for compiling and testing of data mining algorithms.
It supports all data mining algorithms, data types, file formats, distance and similarity functions. It is possible to use a combination of all existing components in a project.
The ELKI Java-Library (EJL) is the complete Java library for all Java data mining frameworks. It is used for all toolkits and projects by ELKI

Mac OS X 10.5.7 or higher
1024 x 768 or higher resolution
80 MB or higher of hard drive space
DirectX 9.0 compatible video card
DirectX 9.0 compatible sound card
Minimum 128MB VRAM
Minimum 2GB RAM
OS X 10.7 or higher is required
DirectX 11.0 compatible video card
DirectX 11.0 compatible sound card
Minimum 256MB VRAM

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