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Essay / Research Paper Abstract
This 15 page paper provides an overview of the issue presented and relates it to the current literature. As technological advancements enter almost every avenue of business development, personalized web-based systems have been created to tailor business operations to the specific needs of the user. Personalized web-based systems make use of artificial intelligence (AI), specifically, intelligent techniques to provide individual users with content tailored to their specific needs. As a result, a number of different data mining techniques have been created to support the personalization of web-based operations. Bibliography lists 5 sources.
Page Count:
15 pages (~225 words per page)
File: MH11_MHDatMin.rtf
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Unformatted sample text from the term paper:
to the specific needs of the user. Personalized web-based systems make use of artificial intelligence (AI), specifically, intelligent techniques to provide individual users with content tailored to their specific
needs. As a result, a number of different data mining techniques have been created to support the personalization of web-based operations. These data mining techniques can be classified
as supervised learning (i.e. classification) and unsupervised learning (i.e. clustering), and variations in forms of data mining result in specific gains and limitations. Web developers have recognized that different
data mining systems can be beneficial for different user needs, but limitations still exist in the process of personalization. In assessing the topic of personalization and the specific use
of data mining through artificial intelligence, it is necessary to understand the varied types of data mining systems and the underlying factors influencing their utility. One of the greatest
areas of consideration when developing data mining systems is that some argue that data mining produces results that are comparative to traditional statistical methods, including clustering. As a result,
assessing the difference between data mining and traditional statistical methods is one means of understanding the way in which data mining functions and how it influences outcomes in specific business
or market structures. The student should integrate the following elements when creating their own paper: Problem Description Data
Mining is technically the utilization of algorithmic methodology to gain knowledge through discovery by combing through vast amounts of data (Ozkural, 2002). Data mining goes hand-in-hand on a
conceptual level with traditional statistical methods, including clustering, because data mining is based on the idea that the integration of vast amounts of data can be beneficial as a tool
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