XML-based visualization and evaluation of data mining results

2007 ◽  
pp. 319-333
Author(s):  
Dietrich Wettschereck
Keyword(s):  
Data Mining ◽  
Evaluation Of Data

Evaluation of Data Mining Methods

2011 ◽  
pp. 464-468
Author(s):  
Paolo Giudici
Keyword(s):  
Data Mining ◽  
Linear Regression ◽  
Bayesian Networks ◽  
Statistical Methods ◽  
Linear Regression Models ◽  
Explanatory Variables ◽  
Tree Models ◽  
Number Of Leaves ◽  
Mining Methods ◽  
Evaluation Of Data

Several classes of computational and statistical methods for data mining are available. Each class can be parameterised so that models within the class differ in terms of such parameters (see, for instance, Giudici, 2003; Hastie et al., 2001; Han & Kamber, 2000; Hand et al., 2001; Witten & Frank, 1999): for example, the class of linear regression models, which differ in the number of explanatory variables; the class of Bayesian networks, which differ in the number of conditional dependencies (links in the graph); the class of tree models, which differ in the number of leaves; and the class multi-layer perceptrons, which differ in terms of the number of hidden strata and nodes. Once a class of models has been established the problem is to choose the “best” model from it.

scholarly journals Evaluation of Rapidminer-Aplication in Data Mining Learning using PeRSIVA Model

2020 ◽  
Vol 4 (2) ◽  
pp. 164-171
Author(s):  
Muhammad Uska ◽  
◽  
Rasyid Wirasasmita ◽  
Usuluddin Usuluddin ◽  
Baiq Arianti ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Data Collection ◽  
Completion Rate ◽  
Descriptive Statistics ◽  
Think Aloud ◽  
Process Data ◽  
Data Collection Technique ◽  
Evaluation Of Data ◽  
Analyze Data

RapidMiner is an application that is used to analyze data quantities and qualitatively to obtain information and knowledge as expected. This software is implemented to process data using several methods or algorithms in Data Minig learning. However, when using this software, users sometimes cannot distinguish between various methods or algorithms in Data Mining. Therefore, it is necessary to evaluate to optimize the use of this software in data mining learning. This study focuses on RapidMiner evaluation of data mining learning using the Persiva model. This model consists of aspects of satisfaction, behavior, impact, and effectiveness. The data collection technique was in the form of a questionnaire with 48 subjects. Data analysis used is descriptive statistics to determine satisfaction, behavior and effects. Meanwhile, Think-Aloud Retrospective technique is used to determine the effectiveness of RapidMiner. Our findings show that users are satisfied with the results of respondents on average agreeing (80%), in the aspect of behavior and impact, the percentage results are above 80%, and the use of this application has been effective with a completion rate above 90%. So it can be concluded that by using this application in data mining learning users can easily complete tasks, and be motivated, and add insights and knowledge in relevant disciplines.

Evaluation of data mining approaches for the control of multifunctional arm prostheses

2011 ◽  
Vol 18 (3) ◽  
pp. 235-249 ◽  
Author(s):  
Markus Reischl ◽  
Lutz Gröll ◽  
Ralf Mikut
Keyword(s):  
Data Mining ◽  
Evaluation Of Data

Evaluation of Data Mining Methods

2011 ◽  
pp. 789-794 ◽  
Author(s):  
Paolo Giudici
Keyword(s):  
Data Mining ◽  
Linear Regression ◽  
Bayesian Networks ◽  
Statistical Methods ◽  
Linear Regression Models ◽  
Explanatory Variables ◽  
Tree Models ◽  
Number Of Leaves ◽  
Mining Methods ◽  
Evaluation Of Data

Several classes of computational and statistical methods for data mining are available. Each class can be parameterised so that models within the class differ in terms of such parameters (See for instance Giudici, 2003, Hastie et al., 2001, Han and Kamber, 200, Hand et al, 2001 and Witten and Frank, 1999). For example the class of linear regression models, which differ in the number of explanatory variables; the class of bayesian networks, which differ in the number of conditional dependencies (links in the graph); the class of tree models, which differ in the number of leaves and the class multi-layer perceptrons which differ in terms of the number of hidden strata and nodes. Once a class of models has been established the problem is to choose the “best” model from it.

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