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Relational Knowledge Discovery, by M. E. Müller
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What is knowledge and how is it represented? This book focuses on the idea of formalising knowledge as relations, interpreting knowledge represented in databases or logic programs as relational data and discovering new knowledge by identifying hidden and defining new relations. After a brief introduction to representational issues, the author develops a relational language for abstract machine learning problems. He then uses this language to discuss traditional methods such as clustering and decision tree induction, before moving onto two previously underestimated topics that are just coming to the fore: rough set data analysis and inductive logic programming. Its clear and precise presentation is ideal for undergraduate computer science students. The book will also interest those who study artificial intelligence or machine learning at the graduate level. Exercises are provided and each concept is introduced using the same example domain, making it easier to compare the individual properties of different approaches.
- Brand: Brand: Cambridge University Press
- Published on: 2012-07-30
- Original language: English
- Number of items: 1
- Dimensions: 9.72" h x .71" w x 6.85" l, 1.45 pounds
- Binding: Hardcover
- 278 pages
- Used Book in Good Condition
About the Author
M. E. Mueller is a Professor of Computer Science at the Bonn-Rhein-Sieg University of Applied Sciences.
Most helpful customer reviews
1 of 1 people found the following review helpful.
Good range of topics in a small volume.
By DC NY
I started this several years ago, and very much appreciated the breadth of material in such a small volume.
But everyone learns and explores and plays with abstractions, in a way that's comfortable for them.
For my part, I need links. I need frequent referrals back to previously covered topics, lots of repetition & review. Although the professor provides sufficient background & review material in the early chapters, there's no substitute for being able to follow links to look up topics previously elaborated upon.
So, I found myself getting to the next topic, not recalling a definition and having to search through back pages or the index to find it.
Another student might find this easy. No question, Müller's contribution deserves the 5 stars. But it was just so inconvenient for me, going back & forth, that eventually the book had to go back on the shelf.
If there's ever a Kindle or PDF version, I'm in. I'm willing to pay the same price for that, as the cost of the soft cover book.
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