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CS404_Exam_1_092501_bob - CSc 401 Data Mining Exam#1 Name...

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CSc 401 – Data Mining Name:______________________________ Exam #1 September 25, 2001 Score:_________________/100 Directions: Carefully answer each of the following questions. This is an open-book, open-note exam. You may use calculators. You are NOT to get help from others. Points will be assigned on answer quality as well as answer correctness. CLEARLY show all work. 1. Characterize the difference between on-line analytical processing (OLAP) and data mining. [5 pts.] data mining is driven by data while OLAP is driven by the user or the user's intention to verify his or  her queries OLAP is efficient in reporting on data while data mining focuses on finding patterns in data. OLAP is  a top-down approach to data analysis since it basically comes down to the operation of data via  addition. In other words, OLAP applications typically operate on summary data that have been  aggregated in complex tables for fast and easy analysis. With an OLAP application, a marketing  manager might slice through a segment of recent high-value customers to report on what they're  buying, how much they're spending and how much time they typically spend at the company site.  Data mining algorithm, on the other hand, runs on sales and visit data to discover patterns of  behavior in the high-value customers. Data mining can find what factors influence certain types of  sales most. Therefore, it can be said that data mining operates on division of data rather than  summation http://www.ciadvertising.org/student_account/fall_00/adv391k/hyojin/termpaper/comparison .html The main difference between OLAP and data mining is how they operate on the data. OLAP tools provide multidimensional data analysis--that is, they allow data to be broken down and summarized (such as by regional sales). For example, OLAP typically involves the summation of multiple databases into highly complex tables. OLAP tools deal with aggregates--OLAP technology basically comes down to the operation of data via addition. For example, OLAP can tell you about the total number of widgets sold in all the ZIP codes in the country. Data mining, on the other hand, is about ratios, patterns and influences in a data set. As such, data mining is division. Data mining can tell you about the factors influencing the sales of the widgets in those ZIP codes. This is not to say that both OLAP and data mining should not be used in conjunction to gain a powerful insight into your company databases, customer information file, data marts and data warehouse. In fact, aggregate and inductive analyses can complement each other. For example, a data mining analysis can discover a significant relationship in a set of attributes. OLAP can then expand on this and generate a report detailing the impact of the discovery.
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