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Weka-A Machine Learning Workbench for Data Mining

Weka-A Machine Learning Workbench for Data Mining,10.1007/978-0-387-09823-4_66,Eibe Frank,Mark Hall,Geoffrey Holmes,Richard Kirkby,Bernhard Pfahringer

Weka-A Machine Learning Workbench for Data Mining   (Citations: 5)
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The Weka workbench is an organized collection of state-of-the-art ma- chine learning algorithms and data preprocessing tools. The basic way of interacting with these methods is by invoking them from the com- mand line. However, convenient interactive graphical user interfaces are provided for data exploration, for setting up large-scale experiments on distributed computing platforms, and for designing configurations for streamed data processing. These interfaces constitute an advanced en- vironment for experimental data mining. The system is written in Java and distributed under the terms of the GNU General Public License. Experience shows that no single machine learning method is appropriate for all possible learning problems. The universal learner is an idealistic fantasy. Real datasets vary, and to obtain accurate models the bias of the learning algorithm must match the structure of the domain. The Weka workbench is a collection of state-of-the-art machine learn- ing algorithms and data preprocessing tools. It is designed so that users can quickly try out existing machine learning methods on new datasets
Published in 2010.
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    • ...Data mining toolsets such as work done by [5] offer implementations of the most common data mining algorithms...

    Larry Proctoret al. Analytical Pathway Methodology: Simplifying Business Intelligence Cons...

    • ...Both methods are supported by the first Semantic Trajectory Data Mining Query Language (ST-DMQL) [8], implemented in PostGIS (spatial database extension for PostgreSQL) and Weka[22], in order to instantiate the proposed model...

    Vania Bogornyet al. A Conceptual Data Model for Trajectory Data Mining

    • ...The phrasal patterns for questi ons were derived by employing the algorithm (Figure 5) explained in Section 4, with the following parameter settings: (maximum token gap) � = 5, (minimum support threshold) minSup = 5 and (maximum number of items in a pattern)N = 5. We generated the question clusters for agents’ and customers’ questions separat ely using k = 30 in the k-means implementation in Weka [17]...
    • ...We used the Weka toolkit [17] for clustering the questions...
    • ...We illustrate the results using two types of classification methods‐ the Naive-Bayes C lassifier and Support Vector machines implemented in Weka [17]...

    Anup Chalamallaet al. Identification of class specific discourse patterns

    • ...A developer might use one a machine learning software package created to encapsulate a variety of 720 K. Lyons et al. algorithms such as Weka [1] or Matlab...
    • ...An excellent candidate for this expansion would be the Weka machine learning library, which includes implementations for a variety of different algorithms [1]...

    Kent Lyonset al. GART: The Gesture and Activity Recognition Toolkit

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