Titlebook: Data Mining; 16th Australasian Co Rafiqul Islam,Yun‘Sing Koh,Zahidul Islam Conference proceedings 2019 Springer Nature Singapore Pte Ltd. 2

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Wolfgang Goerigk,Friedemann Simonso known as HDLSS problem), non-linearity, and data types tend are complex. A large number of dimensionality reduction techniques developed, but, unfortunately, not efficient with small-sample (observation) size dataset. To overcome the pitfalls of the sample-size and dimensionality this study emplo

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https://doi.org/10.1007/10703260nominal and numerical attributes. However, their need to test the suitability of every attribute at every tree node, in addition to testing every possible split-point for every numerical attribute can be expensive computationally, particularly for datasets with high dimensionality. This paper propos

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Carole Bouchard,Jean-François Omhoverutant exposure has generally been wanting. In recent years this has motivated the development of several cheap, portable air quality monitoring instruments. However, these instruments also tend to be unreliable, and thus the raw measurements require preprocessing to make accurate predictions of actu

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and the Encounter of Performanceis paper proposes a new hybrid imputation method to effectively deal with the missing data issue of the Mobility in Cities Database (MCD) to construct city mobility indices. The hybrid method integrates the advantages of decision trees and fuzzy clustering into an iterative algorithm for missing dat

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