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Full-Text Articles in Physical Sciences and Mathematics
A Wrapper-Based Feature Selection For Analysis Of Large Data Sets, Jinsong Leng, Craig Valli, Leisa Armstrong
A Wrapper-Based Feature Selection For Analysis Of Large Data Sets, Jinsong Leng, Craig Valli, Leisa Armstrong
Leisa Armstrong
Knowledge discovery from large data sets using classic data mining techniques has been proved to be difficult due to large size in both dimension and samples. In real applications, data sets often consist of many noisy, redundant, and irrelevant features, resulting in degrading the classification accuracy and increasing the complexity exponentially. Due to the inherent nature, the analysis of the quality of data sets is difficult and very limited approaches about this issue can be found in the literature. This paper presents a novel method to investigate the quality and structure of data sets, i.e., how to analyze whether there …
Application Of A Data Mining Framework For The Identification Of Agricultural Production Areas In Wa , Yunous Vagh, Leisa Armstrong, Dean Diepeveen
Application Of A Data Mining Framework For The Identification Of Agricultural Production Areas In Wa , Yunous Vagh, Leisa Armstrong, Dean Diepeveen
Leisa Armstrong
This paper will propose a data mining framework for the identification of agricultural production areas ill WA. The data mining (DM) framework was developed with the aim of enhancing the analysis of agricultural datasets compared to currently used statistical methods. The DM framework is a synthesis of different technologies brought together for the purpose of enhancing the interrogation of these datasets. The DM framework is based on the data, information, knowledge and wisdom continuum as a horizontal axis, with DM and online analytical processing (OLAP) forming the vertical axis. In addition the DM framework incorporates aspects of data warehousing phases, …