Data cleaning

Data Mining is the core of the more comprehensive process of knowledge dis-coveiy in data bases (KDD). However, the term data mining" is often used synonymously with KDD. KDD describes the process of extracting and storing data and also includes methods for data preparation such as data cleaning, data selection, and data transformation as well as evaluation, presentation, and visualization of the results after the data mining process. 

FIGURE 4.18 Response versus fitted values for the ash data. Left OLS for original data the symbol I indicated outliers identified with robust regression. Right OLS regression for cleaned data where outliers are excluded. 

This lack of control can make it difficult for physician-owned SMOs to ensure quality performance across all sites. If a site is slow to enroll patients or generating CRFs with a high incidence of errors, physician-owned SMOs are not necessarily empowered to quickly improve an individual site s performance. This is particularly troublesome in light of the fact that in a large multicenter trial, success hinges on the worst-performing site. Data cannot be locked and analyzed until all the sites are closed. Physician-owned SMOs may not have the same ability to conduct studies quickly and produce clean data as do the wholly owned SMOs. 

At study completion, the data manager is responsible for assuring that the data are clean and then prepares to lock the study/close the database. The purpose of locking the study is to ensure that a full audit trail of any changes exists once the study/patients have been unblinded. Database closure marks the end of the study conduct phase and the beginning of the analysis and reporting phase. The standard definition of clean data is ... 

Time plan Providing a time frame for the preliminary model building and the production of clean file, clean data sets, the analysis, and the report-writing... 

Tension Homogeneous Clean data Wind-up is easiest to generate Requires high viscosity Sample gripping Low e Sample history, preparation Need bath... 

It appears that we have placed so much emphasis on certainty that we are uncomfortable with uncertainty. In handling data, we tend to avoid and hide the uncertainties in our obsession to produce "clean" data. But clean data are more a matter of judgment than of actuality. Raw data are frequently disorderly in the sense that they are full of perturbations resulting from the many outside influences on the particular property we are measuring. The value which we obtain at any given moment is equivalent to a series of one-frame still pictures from a continuously running movie film. As a result of this discontinuous sampling of a... 

The input to AIPS is the dirty image (in this case the dirty data cube) and the dirty beam. After applying the CLEAN algorithm for each spectral channel, the CLEAN data cube is obtained. 

By performing a wavenumber integration of the CLEAN data cube, it is easier to detect the three sources of the Master sky map. The results are shown in Fig. 5.17 (left). By representing the logarithm of the integration (right) the three sources are... 

The strength of the technique lies in its speed and degree of automation (samples can be loaded in 96-well plates). However, our experience has shown that successful automated data analysis requires relatively clean data. For this reason we expect improvement in efficiency once a set of cosolvent buffer reagents becomes available. 

Like automatic line cleaning, data logging technologies would be anticipated to add real value in terms of understanding key success parameters and diagnostics such as... 

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