Business Decision Making
Question 1: Define data mining.
Answer 1: Data mining is a function related to data warehousing in which an automated process searches through data looking for specific relationships. Users may or may not be aware of these relationships—often data mining is used to discover relationships the user has not yet found. The data mining process finds these relationships and then presents them to the user in such a way that the user can easily view and analyze the resulting data. Data mining has become more and more important in larger companies as techniques have become more refined. Early data mining often returned data that did not accurately represent the desired relationships, or data that was for the most part meaningless. More sophisticated data mining processes have made this a vital part of the data warehousing system for any company.
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Question 2: Define hypothesis verification in the context of data mining.
Answer 2: A traditional approach to database query, hypothesis verification retrieves data from the data warehouse based on specific requests by the user. The user inputs certain criteria for the data he or she wishes to retrieve. These criteria can represent several dimensions of the desired data. Based on the request, the system returns lists of data. For example, the user might ask for customers that fit a certain demographic.The process is referred to as hypothesis verification because the data returned is based on the hypothesis by the user that this data will be the best suited for his eventual use. A user might hypothesize that a certain marketing effort will appeal most to women between the ages of 35 and 45, and thus request that information. The strength of the data returned to solve the problem is directly related to the user’s hypothesis.
Question 3: Define knowledge discovery in the context of data mining.
Answer 3: Knowledge discovery requires the data warehousing system to be able to analyze the data beyond the requests of an individual user. This type of data mining makes use of processes that can find data for a specific request that the user might not have thought to request. Rather than responding to very specific criteria, the software that mines the data will use patterns it perceives in the data to return a wider variety of data than the user might have initially anticipated. For example, a query the user might have thought would be most applicable to women aged 35 to 45 might also return data on men aged 45 to 55 because the software sees similar spending patterns in this group. Knowledge discovery uses artificial intelligence tools to make these connections.
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