Data hiding within audio signals by Petrovic, Winograd, Jemili, Metois

By Petrovic, Winograd, Jemili, Metois

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ACSys Data Mining 47 Plug-Ins Visualise VR Cave Workbench GUI Execute XML XML XML Data Extraction Agent Schema Statistical Summary Visualise Associatns Fast Summaries XML Regression Rules Java SEF/JDBC Data Access API Read Many Data Cubes Write Rarely Fast Data Cube Generation (proposed Java SEF) RDBMS Fig. 7. A schematic of the architecture of the Data Miner’s Arcade. Three APIs are identified, providing access to the data, the GUI interfaces, and output of the models. Whilst there are a number of systems that have provided such a unified interface to a variety of tools, the architecture of The Data Miner’s Arcade has a number of distinct advantages and features.

All are performing similar and related tasks, yet the results need to be carefully understood in the context of the tool used to generate the results. Tuning them requires different interactions with (sometimes very) different interfaces, and simply getting the data into a form that the tool can process requires many careful transformations. While sometimes seeming trivial, these are at best annoyances, and at worst they significantly inhibit the data mining process. The first task in a data mining project, once the data is made available in some format, is to transform that data into a format suitable for each of the data mining tools to be used.

The required modification will be discussed briefly, below. In a nutshell, the original MARS is an efficient technique designed to select a (relatively high quality) model from the space of multivariate piecewise linear functions1 . kd (x): K1 Kd ... , d. , Kj are certain prespecified knot locations on the variable xj taken to be, for example, quantiles of the corresponding marginal distribution of the data points. kd can be determined based the least squares fit of the general model (3) to the data at hand.

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