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Semantic Data Mining

Semantic Data Mining

Protima Banerjee, Xiaohua Hu, Illhio Yoo
Copyright: © 2008 |Pages: 7
ISBN13: 9781599049519|ISBN10: 1599049511|EISBN13: 9781599049526
DOI: 10.4018/978-1-59904-951-9.ch221
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MLA

Banerjee, Protima, et al. "Semantic Data Mining." Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications, edited by John Wang, IGI Global, 2008, pp. 3524-3530. https://doi.org/10.4018/978-1-59904-951-9.ch221

APA

Banerjee, P., Hu, X., & Yoo, I. (2008). Semantic Data Mining. In J. Wang (Ed.), Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications (pp. 3524-3530). IGI Global. https://doi.org/10.4018/978-1-59904-951-9.ch221

Chicago

Banerjee, Protima, Xiaohua Hu, and Illhio Yoo. "Semantic Data Mining." In Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications, edited by John Wang, 3524-3530. Hershey, PA: IGI Global, 2008. https://doi.org/10.4018/978-1-59904-951-9.ch221

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Abstract

Over the past few decades, data mining has emerged as a field of research critical to understanding and assimilating the large stores of data accumulated by corporations, government agencies, and laboratories. Early on, mining algorithms and techniques were limited to relational data sets coming directly from Online Transaction Processing (OLTP) systems, or from a consolidated enterprise data warehouse. However, recent work has begun to extend the limits of data mining strategies to include “semi-structured data such as HTML and XML texts, symbolic sequences, ordered trees and relations represented by advanced logics” (Washio & Motoda, 2003).

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