By Inmon W.H.
Some of the most very important makes use of of the knowledge warehouse is that of information mining. facts mining is the method of utilizing uncooked info to deduce vital company relationships. as soon as the enterprise relationships were came upon, they could then be used for company virtue. definitely a knowledge warehouse has different makes use of than that of knowledge mining. notwithstanding, the fullest use of a knowledge warehouse definitely needs to contain facts mining.There are many methods to information mining simply as there are numerous ways to genuine mining of minerals. Minerals traditionally were mined in lots of alternative ways - by means of panning for gold, digging mine shafts, strip mining, interpreting satellite tv for pc pictures taken from area, and so forth.In a lot a similar type, information mining happens in lots of varied kinds and flavors, each one with their very own overhead, rewards and likelihood of good fortune.
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Extra info for Data Mining: an Architecture
Example text
Type A type B type C another good way to achieve an interesting perspective of data is to categorize data into classes and count the occurrences of data in each class. 38 shows that data is grouped into classes. Once grouped into classes, the DSS analyst does studies on the classes of data rather on the raw data itself. In such a fashion, data can be studied and managed efficiently and succinctly. [This Tech Topic is Part 1 in the series on data mining and data exploration. In this Tech Topic we have explored the origins of data mining, the considerations of the infrastructure of data mining and the activities of exploration.
38 shows this approach. type A type B type C another good way to achieve an interesting perspective of data is to categorize data into classes and count the occurrences of data in each class. 38 shows that data is grouped into classes. Once grouped into classes, the DSS analyst does studies on the classes of data rather on the raw data itself. In such a fashion, data can be studied and managed efficiently and succinctly. [This Tech Topic is Part 1 in the series on data mining and data exploration.
38 shows that data is grouped into classes. Once grouped into classes, the DSS analyst does studies on the classes of data rather on the raw data itself. In such a fashion, data can be studied and managed efficiently and succinctly. [This Tech Topic is Part 1 in the series on data mining and data exploration. In this Tech Topic we have explored the origins of data mining, the considerations of the infrastructure of data mining and the activities of exploration. ] 30 © 1997 William H.