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Information mining implies using powerful and sophisticated tools to do the following:
Uncover associations, patterns and trends
Group and classify information
Develop predictive models
From a technical perspective, the real keys to successful information mining are its algorithms. Algorithms enable an information mining application to determine whom the best customers for the business are or what they like to buy. They can also determine at what time of the day, in what combinations, or how an organization can optimize inventory, pricing, and merchandising in order to retain these customers and cause them to buy more, at increased profit margins (Anderson, 2000, p. 459).
Text mining is a key technology that enables knowledge management and is analogous to data mining in that it uncovers relationships in information. Although it is analogous to data mining, it is different. Indeed, data mining is the application of statistical and machine learning algorithms to a set of data to uncover previously unidentified connections and correlations. Unlike data mining, text mining works with information stored in an unstructured collection of text documents. Specifically, online text mining refers to the process of searching through unstructured data on the Internet and deriving some meaning from it. Text mining goes beyond applying statistical models to data files: in fact, text mining uncovers relationships in a text collection and leverages the creativity of the knowledge worker to explore these relationships and discover new knowledge. Many text-mining algorithms help in the discovery of new knowledge by complementing the ideas and logic that exist within the worker. Text mining is particularly relevant today because of the enormous amount of knowledge that resides in text documents that are stored either within the organization or outside of it (Anderson, 2000, p. 461). Benefits of text mining include:
Increasedvalue of corporate information
Lower integration costs versus other text-processing technology
Increased productivity of knowledge workers
Text mining can be used wherever there is a large amount of text that needs to be analyzed, such as in e-mail management, document management, automated help desk, market research and business intelligence gathering (Anderson, 2000, p. 464).
A semantic network provides a concise and very accurate summary of the analyzed text. Analogous to the artificial neural networks, each element of the semantic network-a concept-is characterized by its weight and a set of relationships to other elements of the network-a context node. Each relationship between elements of the network is assigned a weight as well (Anderson, 2000, p. 465).
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