By Soumen Chakrabarti, et al
A part of the worth of a publication like this is often to get correct, well-focused tips into the bigger literature. a few chapters have "resources" sections; a few "resources" sections have bibliographies. there isn't any constant trend to the insurance. eventually, and such a lot harmful, many assets are easily lacking: "Hartigan, 1975" is all you will get to determine extra approximately diversifications of k-means clustering.
I desire the writer will rectify this by way of offering sufficient bibliographical fabrics on their web site or in later variants.
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Example text
4 MACHINE LEARNING AND STATISTICs What’s the difference between machine learning and statistics? Cynics, looking wryly at the explosion of commercial interest (and hype) in this area, equate data mining to statistics plus marketing. In truth, you should not look for a dividing line between machine learning and statistics because there is a continuum—and a multidimensional one at that—of data analysis techniques. Some derive from the skills taught in standard statistics courses, and others are more closely associated with the kind of machine learning that has arisen out of computer science.
4 MACHINE LEARNING AND STATISTICs What’s the difference between machine learning and statistics? Cynics, looking wryly at the explosion of commercial interest (and hype) in this area, equate data mining to statistics plus marketing. In truth, you should not look for a dividing line between machine learning and statistics because there is a continuum—and a multidimensional one at that—of data analysis techniques. Some derive from the skills taught in standard statistics courses, and others are more closely associated with the kind of machine learning that has arisen out of computer science.
There followed a rash of business-oriented books on data mining, focusing mainly on practical aspects of how it can be put into practice with only superficial descriptions of the technology that underlies the methods used. They are valuable sources of applications and inspiration. For example, Adriaans and Zantige (1996) from Syllogic, a European systems and database consultancy, provide an early introduction to data mining. Berry and Linoff (1997), from a Pennsylvania-based company specializing in data warehousing and data mining, give an excellent and example-studded review of data mining techniques for marketing, sales, and customer support.