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Click to edit Master subtitle style 10/2/11 Business Intelligence Software and Techniques Syam Menon MIS 6324
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10/2/11 What is Data Mining? Many Definitions Extraction of interesting (previously unknown and potentially useful) patterns or knowledge from large volumes of data. aka Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc. 22
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10/2/11 Why Mine Data? Data mining permits organizations to make the most effective use of the vast amounts of data that they have gathered about customers, suppliers and industry trends. Data exists Large volumes of data being collected and stored Web data, e-commerce Purchases at department/grocery stores Bank/Credit Card transactions Only small portion (~5-10%) of collected data is analyzed Data that may never be analyzed is collected in the fear that something that may prove important will be missed Feasible to do – computers are cheaper and more powerful Not much choice – competition is strong Providing better, customized services gives an edge 33
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10/2/11 Necessity Drives Innovation Data explosion is a problem Automated data collection tools and mature database technology ¶ a lot of data accumulated in databases, data warehouses, and other information repositories A lot of data, limited knowledge Solution: Data warehousing and data mining Data warehousing and on-line analytical processing Mining interesting knowledge (rules, regularities, patterns, constraints) from data in large databases 44
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10/2/11 Data Mining Applications Target marketing Have large data set of potential customers Only 1% are really interested in your product Can you limit promotions to only those who are likely to respond? Find clusters of “model” customers who share the same characteristics interests, income level, spending habits, etc. Determine customer purchasing patterns over time Related issue: Customer retention/churning 55
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10/2/11 Data Mining Applications Cross-market analysis Associations/co-relations between product sales, prediction based on such associations E.g. Amazon’s recommendations/personalization Market Basket Analysis (product level): items bought together Collaborative filtering (customer level): customers like you also bought… 66
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10/2/11 Data Mining Applications Customer profiling What types of customers buy what products (clustering or classification) Customer requirement analysis Identifying the best products for different customers Predict what factors will attract new customers Provision of summary information Multidimensional summary reports Statistical summary information (data central tendency and variation) 77
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10/2/11 Data Mining Applications Risk Analysis Scoring credit card applications Assessing customer risk level for insurance premium
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This note was uploaded on 10/02/2011 for the course MIS 6312 taught by Professor Wiorkowski during the Spring '11 term at University of Texas at Dallas, Richardson.

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mis6324introductionI - Click to edit Master subtitle style...

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