The choices you make in setting up your data mining

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Unformatted text preview: e workings of the tools you choose and the algorithms on which they are based. The choices you make in setting up your data mining tool and the optimizations you choose will affect the accuracy and speed of your models. Data mining does not replace skilled business analysts or managers, but rather gives them a powerful new tool to improve the job they are doing. Any company that knows its business and its customers is already aware of many important, high-payoff patterns that its employees have observed over the years. What data mining can do is confirm such empirical observations and find new, subtle patterns that yield steady incremental improvement (plus the occasional breakthrough insight). Data mining and data warehousing Frequently, the data to be mined is first extracted from an enterprise data warehouse into a data mining database or data mart (Figure 1). There is some real benefit if your data is already part of a data warehouse. As we shall see later on, the problems of cleansing data for a data warehouse and for data mining are very similar. If the data has already been cleansed for a data warehouse, then it most likely will not need further cleaning in order to be mined. Furthermore, you will have already addressed many of the problems of data consolidation and put in place maintenance procedures. The data mining database may be a logical rather than a physical subset of your data warehouse, provided that the data warehouse DBMS can support the additional resource demands of data mining. If it cannot, then you will be better off with a separate data mining database. Data Warehouse Data Sources Geographic Data Mart Analysis Data Mart Data Mining Data Mart Figure 1. Data mining data mart extracted from a data warehouse. 2 © 1999 Two Crows Corporation A data warehouse is not a requirement for data mining. Setting up a large data warehouse that consolidates data from multiple sources, resolves data integrity problems, and loads the data into a query database can be an enormous task, sometimes taking years and costing millions of dollars. You could, however, mine data from one or more operational or transactional databases by simply extracting it into a read-only database (Figure 2). This new database functions as a type of data mart. Data Sources Data Mining Data Mart Figure 2. Data mining data mart extracted from operational databases. Data mining and OLAP One of the most common questions from data processing professionals is about the difference between data mining and OLAP (On-Line Analytical Processing). As we shall see, they are very different tools that can complement each other. OLAP is part of the spectrum of decision support tools. Traditional query and report tools describe what is in a database. OLAP goes further; it’s used to answer why certain things are true. The user forms a hypothesis about a relationship and verifies it with a series of queries against the data. For example, an analyst might want to determine the factors that lead to loan defaults. He or she might initially hypothesize that people with low incomes are bad credit risks and analyze the database with OLAP to verify (or disprove) this assumption. If that hypothesis were not borne out by the data, the analyst might then look at high debt as the determinant of risk. If the data did not support this guess either, he or she might then try debt and income together as the best predictor of bad credit risks. In other words, the OLAP analyst generates a series of hypothetical patterns and relationships and uses queries against the database to verify them or disprove them. OLAP analysis is essentially a deductive process. But what happens when the number of variables being analyzed is in the dozens or even hundreds? It becomes much more difficult and time-consuming to find a good hypothesis (let alone be confident that there is not a better explanation than the one found), and analyze the database with OLAP to verify or disprove it. Data mining is different from OLAP because rather than verify hypothetical patterns, it uses the data itself to uncover such patterns. It is essentially an inductive process. For example, suppose the analyst who wanted to identify the risk factors for loan default were to use a data mining tool. The data mining tool might discover that people with high debt and low incomes were bad credit risks (as above), but it might go further and also discover a pattern the analyst did not think to try, such as that age is also a determinant of risk. Here is where data mining and OLAP can complement each other. Before acting on the pattern, the analyst needs to know what the financial implications would be of using the discovered pattern to govern who gets credit. The OLAP tool can allow the analyst to answer those kinds of questions. Furthermore, OLAP is also complementary in the early stages of the knowledge discovery process because it can help you explore your data, for instance by focusing attention on important variables, © 1999 Tw...
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