Data clean

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. 

Of all the requirements that have to be fulfilled by a manufacturer, starting with responsibilities and reporting relationships, warehousing practices, service contract policies, airhandUng equipment, etc., only a few of those will be touched upon here that directly relate to the analytical laboratory. Key phrases are underlined or are in italics Acceptance Criteria, Accuracy, Baseline, Calibration, Concentration range. Control samples. Data Clean-Up, Deviation, Error propagation. Error recovery. Interference, Linearity, Noise, Numerical artifact. Precision, Recovery, Reliability, Repeatability, Reproducibility, Ruggedness, Selectivity, Specifications, System Suitability, Validation. 

Cody s Data Cleaning Techniques Using SAS Software by Ron Cody... 

The data cleaning steps may involve the removal from the data frame of columns (of descriptor values) that are constant or nearly constant, imputing missing values and eliminating columns that are redundant due to a strong relationship with other columns. All these steps are easily automated. Approximate algorithms can easily be developed that are more than 100-fold faster than those available in commercial packages. 

The next step of data cleaning is to perform a replicate and pseudo replicate analysis of the experimental values. When replicate data are available, highly discrepant results can point to problems with the experimental data. When replicate results are not available, pseudo replicates are almost always present in the data. Often the same chemical structure exists more than once in the results file, where the different identifiers refer to different batches of the same material. Thus, a... 

Another aspect of data cleaning arises when data come from different laboratories. Then one is faced with the task of placing the results in a reliable and consistent context (a sort of metaanalysis ). Another data cleaning task involves the imputation (estimation) of missing values. Often the programs that compute descriptors will fail on unusual molecules and then those molecules are usually removed from further consideration. However, sometimes a failure is not a reflection of the desirability of the molecule and imputation of the missing values is then a reasonable strategy. 

Additionally, as errors can easily occur in databases, it cannot be assumed that the data they contain are entirely correct. Even after data cleaning - a process to remove obvious errors and duplicates - there may be inherent errors or mis-classification in the data being collected, particularly if there is subjectivity involved in the measurement that is used. Furthermore, in large, constantly changing databases, there must be rules in place for the data mining algorithm to capture the most current data. 

Study monitoring (including secondary in-house data cleaning and monitoring reports)... 

For objective data, especially when collected in an automated fashion, apply objective data cleaning criteria such as range checks and consistency comparisons. If apparent errors are found that are not simply transcription errors, delve deeply into the reasons and look for systematic errors such as incorrect units, miscalibrated devices, carelessness, or data fraud. 

These examples were meant to illustrate the process that modelers go through in developing models data clean-up, model building, model evaluation, how to use a model once one is developed, etc. Some models can be developed quite simply. Others are quite complex. Are... 

Data cleaning. The means by which databases are purified so that they are fit for analysis. In double-blind trials this will usually take place before the treatment code is broken. An activity which ensures that through automatic error checking, vetting and queries the database will contain only uninteresting truths and plausible lies. 

A further data cleaning was needed at this stage of the analysis, in order to exclude both WTB missing data and the few cases expressing an inconsistent behaviour (WTP > 0 but WTB = 0). There are 1710 remaining valid cases. 

Data were collected from a total of 215 sectors of normal line operations. The data collected during these flights were subjected to a formal data cleaning process to ensure validity. Data cleaning involved the analysis of each error event by members, including representatives of the airline s safety, training, standards and operations departments. This process ensured accuracy in the data-set and that the observers interpretation of erroneons crew actions were accurate, especially with reference to the SOPs of the airline involved in the study. 

Subsequent to data cleaning, further post hoc analysis of the data was undertaken involving the coding of error events according to the taxonomies of error phenotype and genotype described below. 

A software system processes raw point clouds or volumetric data and transfers them into a virtual representation of the object such as surfaces and features. One of the critical tasks of vision-based manufacturing apphcations is to generate a virtual representation and its success rehes on reliable algorithms and tools. Processing of raw scanned data or data cleaning is very important since curves and reconstmcted surfaces are based on the mesh model. Data processing and surface reconstmction is the centre piece of a RE process. The interpretation of raw data to a required computer model is a complicated process, and it involves the following typical issues [10] ... 

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