๐ Visual Data Mining
Wednesday, December 19, 2018 by dev
Visual Data Mining
By:Simeon Simoff,Michael H. Bรถhlen,Arturas Mazeika
Published on 2008-07-23 by Springer
Visual Data MiningโOpening the Black Box Knowledge discovery holds the promise of insight into large, otherwise opaque datasets. Thenatureofwhatmakesaruleinterestingtoauserhasbeendiscussed 1 widely but most agree that it is a subjective quality based on the practical u- fulness of the information. Being subjective, the user needs to provide feedback to the system and, as is the case for all systems, the sooner the feedback is given the quicker it can in?uence the behavior of the system. There have been some impressive research activities over the past few years but the question to be asked is why is visual data mining only now being - vestigated commercially? Certainly, there have been arguments for visual data 2 mining for a number of years โ Ankerst and others argued in 2002 that current (autonomous and opaque) analysis techniques are ine?cient, as they fail to - rectly embed the user in dataset exploration and that a better solution involves the user and algorithm being more tightly coupled.
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Book which was published by Springer since 2008-07-23 have ISBNs, ISBN 13 Code is 9783540710806 and ISBN 10 Code is 3540710809
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