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What is Data Preparation

Chapter 83
Encyclopedia of Information Science and Technology, Second Edition
The data preparation stage involved two stages: data cleaning and transformation. The data cleaning step eliminated inconsistent data. The transformation aims at converting the data fields to numeric fields or vice versa.
Published in Chapter:
Challenges in Data Mining on Medical Databases
Fatemeh Hosseinkhah (Howard University Hospital, USA), Hassan Ashktorab (Howard University Hospital, USA), Ranjit Veen (American University, USA), and M. Mehdi Owrang O. (American University, USA)
Copyright: © 2009 |Pages: 10
DOI: 10.4018/978-1-60566-026-4.ch083
Abstract
Modern electronic health records are designed to capture and render vast quantities of clinical data during the health care process. Technological advancements in the form of computer-based patient records software and personal computer hardware are making the collection of and access to health care data more manageable. However, few tools exist to evaluate and analyze this clinical data after it has been captured and stored. Evaluation of stored clinical data may lead to discovery of trends and patterns hidden within the data that could significantly enhance our understanding of disease progression and management. A common goal of the medical data mining is the detection of some kind of correlation, for example, between genetic features and phenotypes or between medical treatment and reaction of patients (Abidi & Goh, 1998; Li et al., 2005). The characteristics of clinical data, including issues of data availability and complex representation models, can make data mining applications challenging.
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Chapter 69
Data Science in the Database: Using SQL for Data Preparation
Set of activities carried out to prepare data for analysis. Most Data Mining and Machine Learning expect data to be 'clean' (to be well formatted, without missing values, outliers, duplicates or other problems) and to be in a certain format (usually some kind of table). Moreover, some attributes may need special treatment (categorical attributes may need to be standardized or even transformed into numerical; numerical attributes may need to be scaled). Data Preparation is a fundamental activity within the Data Life-Cycle.
Published in Chapter: Data Science in the Database: Using SQL for Data Preparation; From: Encyclopedia of Data Science and Machine Learning
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