Overview of Knowledge Discovery in Databases Process and Data Mining for Surveillance Technologies and EWS

Overview of Knowledge Discovery in Databases Process and Data Mining for Surveillance Technologies and EWS

Inci Batmaz (Middle East Technical University, Turkey) and Güser Köksal (Middle East Technical University, Turkey)
DOI: 10.4018/978-1-61692-865-0.ch001
OnDemand PDF Download:
$37.50

Abstract

Development of more effective early warning systems (EWSs) for various applications have been possible during the past decade due to advancements in information, detection, data mining (DM) and surveillance technologies. These application areas include economy, banking, finance, health care, bioinformatics, production and service delivery, hazard and crime prevention and minimization of other social risks involving the environment, administrations, politics and human rights. This chapter aims to define knowledge discovery in databases (KDD) process in five steps: Data preparation, data preprocessing, DM, evaluation and interpretation, and implementation. DM is further explained in descriptive and predictive mining categories with their functions and methods used or likely to be used in EWSs. In addition to well-known structured data types, mining of advanced data types such as spatial, temporal, sequence, images, multimedia and hypertexts is also introduced. Moreover, it presents a brief survey of overview and application papers and software in the EWS literature.
Chapter Preview
Top

Some Data Mining Applications For Ews Development In The Literature

Several attempts exist in the literature for EWS development in various industries/areas by using DM approaches. These are searched mainly through the Web of Science database, and the following studies are selected to provide an idea for the range and content of the applications.

Complete Chapter List

Search this Book:
Reset