Posts by Category: Data Mining Resources

Updated> Data Mining Resources 2017 Whitepaper Dataset Link Compilation

February 13, 2017

Updated> Data Mining Resources 2017 Whitepaper Dataset Link Compilation
http://www.DataMiningResources.info/

I have just updated my Data Mining Resources 2017 Subject Tracer™ Whitepaper Dataset Link Compilation and it is now a 33 page (287KB) .pdf white paper document is available from the above URL link. It lists alphabetically the latest resources and sources for data mining available from the Internet.[Completely updated with all links validated and new URLs added on February 13, 2017] Additional white papers and resources by Marcus P. Zillman are available by clicking here.

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Updated> Data Mining Resources 2017 Whitepaper Dataset Link Compilation

December 22, 2016

Updated> Data Mining Resources 2017 Whitepaper Dataset Link Compilation
http://www.DataMiningResources.info/

I have just updated my Data Mining Resources 2017 Subject Tracer™ Whitepaper Dataset Link Compilation and it is now a 33 page (287KB) .pdf white paper document is available from the above URL link. It lists alphabetically the latest resources and sources for data mining available from the Internet.[Completely updated with all links validated and new URLs added on December 22, 2016] Additional white papers and resources by Marcus P. Zillman are available by clicking here.

442 views

Updated> Data Mining Resources 2016 Whitepaper Dataset Link Compilation

September 28, 2016

Updated> Data Mining Resources 2016 Whitepaper Dataset Link Compilation
http://www.DataMiningResources.info/

I have just updated my Data Mining Resources 2016 Subject Tracer™ Whitepaper Dataset Link Compilation and it is now a 33 page (306KB) .pdf white paper document is available from the above URL link. It lists alphabetically the latest resources and sources for data mining available from the Internet.[Completely updated with all links validated and new URLs added on September 28, 2016] Additional white papers and resources by Marcus P. Zillman are available by clicking here.

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July 2016 Zillman Column – Data Mining Resources 2016

June 18, 2016

July 2016 Zillman Column – Data Mining Resources 2016
http://columns.virtualprivatelibrary.net/Data_Mining_Resources_2016_July16_Column.pdf
http://www.zillmancolumns.com/

The July 2016 Zillman Column features Data Mining Resources 2016 by Marcus P. Zillman, M.S., A.M.H.A.; Executive Director of the Virtual Private Library. This is a comprehensive listing of data mining directories, subject guides and index resources and sites available on the Internet. Download this excellent freely available 33 page column 288KB today. These resources and sources will help you to discover the many pathways available through the Internet to find the latest data mining resources and sites. This is another MUST have column to discover these data mining resources in today’s ever changing New Economy world!!

This research is powered by Subject Tracer Bots™ available from the Virtual Private Library™.

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Updated> Data Mining Resources 2016 Whitepaper Dataset Link Compilation

June 01, 2016

Updated> Data Mining Resources 2016 Whitepaper Dataset Link Compilation
http://www.DataMiningResources.info/

I have just updated my Data Mining Resources 2016 Subject Tracer™ Whitepaper Dataset Link Compilation and it is now a 33 page (299KB) .pdf white paper document is available from the above URL link. It lists alphabetically the latest resources and sources for data mining available from the Internet.[Completely updated with all links validated and new URLs added on June 1, 2016] Additional white papers and resources by Marcus P. Zillman are available by clicking here.

516 views

MOA (Massive Online Analysis)

May 07, 2016

MOA (Massive Online Analysis)
http://moa.cms.waikato.ac.nz/

MOA is the most popular open source framework for data stream mining, with a very active growing community (blog). It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation. Related to the WEKA project, MOA is also written in Java, while scaling to more demanding problems. MOA performs BIG DATA stream mining in real time, and large scale machine learning. MOA can be extended with new mining algorithms, and new stream generators or evaluation measures. The goal is to provide a benchmark suite for the stream mining community. This will be added to Data mining Resources Subject Tracer™. This will be added to Artificial Intelligence Resources Subject Tracer™.

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SPMF – Open Source Data Mining Library

February 27, 2016

SPMF – Open Source Data Mining Library
http://www.philippe-fournier-viger.com/spmf/

SPMF is an open-source data mining mining library written in Java, specialized in pattern mining. It is distributed under the GPL v3 license. It offers implementations of 112 data mining algorithms for: a) association rule mining; b) itemset mining; c) sequential pattern mining; d) sequential rule mining; e) sequence prediction; f) high-utility pattern mining; and g) clustering and classification. The source code of each algorithm can be integrated in other Java software. Moreover, SPMF can be used as a standalone program with a simple user interface or from the command line. The current version is v0.98 and was released the 14th January 2016. This will be added to Data Mining Resources Subject Tracer™.

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Weka 3: Data Mining Software in Java

February 26, 2016

Weka 3: Data Mining Software in Java
http://www.cs.waikato.ac.nz/~ml/weka/

Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. The name is pronounced like this, and the bird sounds like this. Weka is open source software issued under the GNU General Public License. Yes, it is possible to apply Weka to big data! Data Mining with Weka is a 5 week MOOC, which was held first in late 2013. Check out the MOOC site for video lectures and details on how to enrol into this course and a new, advanced Weka course. This will be added to Data Mining Resources Subject Tracer™. This will be added to Statistics Resources and Big Data Subject Tracer™.
 

 

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DataMelt – Computation and Visualization Environment

February 26, 2016

DataMelt – Computation and Visualization Environment
http://jwork.org/dmelt/

DataMelt is a free mathematics software for scientists, engineers and students. It can be used for numeric computation, statistics, symbolic calculations, data analysis and data visualization. DataMelt, or DMelt, is a software for numeric computation, statistics, analysis of large data volumes (“big data”) and scientific visualization. The program can be used in many areas, such as natural sciences, engineering, modeling and analysis of financial markets. DMelt is a computational platform. It can be used with different programming languages on different operating systems. Unlike other statistical programs, it is not limited by a single programming language. DMelt can be used with several scripting languages, such as Python/Jython, BeanShell, Groovy, Ruby, as well as with Java. Most comprehensive software. It includes more than 30,000 Java classes for computation and visualization. In addition, more than 4000 classes come with Java API, plus 500 Python modules. Not to mention modules of Groovy and Ruby. All libraries are accessed using dynamic scripting. DMelt creates high-quality vector-graphics images (SVG, EPS, PDF etc.) that can be included in LaTeX and other text-processing systems. DataMelt can be used with several scripting languages for the JAVA platform: Jython (Python programming language), Groovy, JRuby (Ruby programming language) and BeanShell. All scripting languages use common DMelt JAVA API. Data analyses and statistical computations can be done in JAVA. Finally, symbolic calculations can be done using Matlab/Octave high-level interpreted language integrated with JAVA. DataMelt runs on Windows, Linux, Mac and Android operating systems. The Android application is called AWork. Thus the software represents the ultimate analysis framework which can be used on any hardware, such as desktops, laptops, netbooks, production servers and android tablets. DataMelt is a portable application. No installation is needed: simply download and unzip the package, and you are ready to run it. One can run it from a hard drive, from a USB flash drive or from any media. DataMelt exists as an open-source portable application, and as JAVA libraries under a commercial friendly license. This will be added to Data Mining Resources Subject Tracer™. This will be added to Statistics Resources and Big Data Subject Tracer™.

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KEEL (Knowledge Extraction based on Evolutionary Learning)

February 26, 2016

KEEL (Knowledge Extraction based on Evolutionary Learning)
http://www.keel.es/

KEEL (Knowledge Extraction based on Evolutionary Learning) is an open source (GPLv3) Java software tool that can be used for a large number of different knowledge data discovery tasks. KEEL provides a simple GUI based on data flow to design experiments with different datasets and computational intelligence algorithms (paying special attention to evolutionary algorithms) in order to assess the behavior of the algorithms. It contains a wide variety of classical knowledge extraction algorithms, preprocessing techniques (training set selection, feature selection, discretization, imputation methods for missing values, among others), computational intelligence based learning algorithms, hybrid models, statistical methodologies for contrasting experiments and so forth. It allows to perform a complete analysis of new computational intelligence proposals in comparison to existing ones. Moreover, KEEL has been designed with a two-fold goal: research and educational. This will be added to Knowledge Discovery Subject Tracer™. This will be added to Data mining Resources Subject Tracer™.

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