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Information Visualization in Data Mining and Knowledge Discovery (The Morgan Kaufmann Series in Data Management Systems) [Hardcover]

Usama Fayyad (Author), Georges Grinstein (Author), Andreas Wierse (Author)
3.5 out of 5 stars  See all reviews (4 customer reviews)


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Book Description

September 3, 2001 The Morgan Kaufmann Series in Data Management Systems


Mainstream data mining techniques significantly limit the role of human reasoning and insight. Likewise, in data visualization, the role of computational analysis is relatively small. The power demonstrated individually by these approaches to knowledge discovery suggests that somehow uniting the two could lead to increased efficiency and more valuable results. But is this true? How might it be achieved? And what are the consequences for data-dependent enterprises?


Information Visualization in Data Mining and Knowledge Discovery is the first book to ask and answer these thought-provoking questions. It is also the first book to explore the fertile ground of uniting data mining and data visualization principles in a new set of knowledge discovery techniques. Leading researchers from the fields of data mining, data visualization, and statistics present findings organized around topics introduced in two recent international knowledge discovery and data mining workshops. Collected and edited by three of the area's most influential figures, these chapters introduce the concepts and components of visualization, detail current efforts to include visualization and user interaction in data mining, and explore the potential for further synthesis of data mining algorithms and data visualization techniques. This incisive, groundbreaking research is sure to wield a strong influence in subsequent efforts in both academic and corporate settings.

* Details advances made by leading researchers from the fields of data mining, data visualization, and statistics.
* Provides a useful introduction to the science of visualization, sketches the current role for visualization in data mining, and then takes a long look into its mostly untapped potential.
* Presents the findings of recent international KDD workshops as formal chapters that together comprise a complete, cohesive body of research.
* Offerss compelling and practical information for professionals and researchers in database technology, data mining, knowledge discovery, artificial intelligence, machine learning, neural networks, statistics, pattern recognition, information retrieval, high-performance computing, and data visualization.



Editorial Reviews

From the Back Cover


Mainstream data mining techniques significantly limit the role of human reasoning and insight. Likewise, in data visualization, the role of computational analysis is relatively small. The power demonstrated individually by these approaches to knowledge discovery suggests that somehow uniting the two could lead to increased efficiency and more valuable results. But is this true? How might it be achieved? And what are the consequences for data-dependent enterprises?


Information Visualization in Data Mining and Knowledge Discovery is the first book to ask and answer these thought-provoking questions. It is also the first book to explore the fertile ground of uniting data mining and data visualization principles in a new set of knowledge discovery techniques. Leading researchers from the fields of data mining, data visualization, and statistics present findings organized around topics introduced in two recent international knowledge discovery and data mining workshops. Collected and edited by three of the area's most influential figures, these chapters introduce the concepts and components of visualization, detail current efforts to include visualization and user interaction in data mining, and explore the potential for further synthesis of data mining algorithms and data visualization techniques. This incisive, groundbreaking research is sure to wield a strong influence in subsequent efforts in both academic and corporate settings.


Features

  • Details advances made by leading researchers from the fields of data mining, data visualization, and statistics.
  • Provides a useful introduction to the science of visualization, sketches the current role for visualization in data mining, and then takes a long look into its mostly untapped potential.
  • Presents the findings of recent international KDD workshops as formal chapters that together comprise a complete, cohesive body of research.
  • Offerss compelling and practical information for professionals and researchers in database technology, data mining, knowledge discovery, artificial intelligence, machine learning, neural networks, statistics, pattern recognition, information retrieval, high-performance computing, and data visualization.

About the Author

Usama Fayyad is co-founder, president, and CEO of digiMine, a data warehousing and data mining ASP. Prior to digiMine, he founded and led Microsoft's Data Mining and Exploration Group, where he developed data mining prediction components for Microsoft Site Server and scalable algorithms for mining large databases.

Georges G. Grinstein is a professor of computer science, director of the Institute for Visualization and Perception Research, and co-director of the Center for Bioinformatics and Computational Biology at the University of Massachusetts, Lowell. He is currently the chief technologist for AnVil Informatics, a data exploration company.

Andreas Wierse is the managing director of VirCinity, a spin-off company of the Computing Centre of the University of Stuttgart. Previously, he worked at the Computer Centre, where he designed and implemented distributed data management for the COVISE visualization system and maintained a wide range of graphics workstations.


Product Details

  • Hardcover: 407 pages
  • Publisher: Morgan Kaufmann; 1st edition (September 3, 2001)
  • Language: English
  • ISBN-10: 1558606890
  • ISBN-13: 978-1558606890
  • Product Dimensions: 9.1 x 7.1 x 1.2 inches
  • Shipping Weight: 2.2 pounds
  • Average Customer Review: 3.5 out of 5 stars  See all reviews (4 customer reviews)
  • Amazon Best Sellers Rank: #2,369,638 in Books (See Top 100 in Books)

 

Customer Reviews

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Average Customer Review
3.5 out of 5 stars (4 customer reviews)
 
 
 
 
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23 of 27 people found the following review helpful:
1.0 out of 5 stars Did the editors ever look at this?, September 5, 2001
By A Customer
This review is from: Information Visualization in Data Mining and Knowledge Discovery (The Morgan Kaufmann Series in Data Management Systems) (Hardcover)
This is very likely the worst book I have ever seen.
Some chapters are barely longer than one (1! and I
am not kidding!) page and merly point to the one
reference, which is - surprise - written by the same
author. There are also chapters that are bit longer
but highly reduandant to other material in the
book - a section on data visualization shows pretty
much the same pictures and graphs than the one on model
visualization. The book does not even attempt to be
consistent or have any flow besides a rough grouping
into a couple of categories.

I find it disturbing that such a bad collection of
obviously non-edited abstracts and papers makes it
into a book. I guess the editors (or publisher?) just
assumed that something with "visualization" and "data
mining" in the title would sell no matter what?

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10 of 11 people found the following review helpful:
4.0 out of 5 stars Some good work but the editing could use some work, September 10, 2001
By A Customer
This review is from: Information Visualization in Data Mining and Knowledge Discovery (The Morgan Kaufmann Series in Data Management Systems) (Hardcover)
As one of the authors of one of the papers, I have mixed feelings about this collection. I think there is some fine work included in the volume, which was the output of a series of workhops looking at the interesection of data mining and data visualization. But I think the publisher (which was changed from Springer to MK after a lengthy delay) and the editors made some mistakes in how the collection was put together. First of all, the book includes the abstracts that workshop participants submitted to gain access to the workhop. As another reviewer indicates, these abstracts are typically very short and often contain content that is included in the longer pieces. The abstracts should have been left out, or at least put into an appendix with an explanation. In addition, there should have been a more significant section providing an overview of the field and a discussion of the opportunities available to the combination of mining and visualization.

That being said, I think the book is a good addition to the field (ignoring the abstract chapters) and describes some interesting ideas by the leaders in the field. I don't think there is any book out there that tackles this subject matter adequately and hopefully this book will help push the state of the art a bit further.

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7 of 9 people found the following review helpful:
4.0 out of 5 stars Editor's note, October 16, 2001
By 
Georges Grinstein (Lowell, MA United States) - See all my reviews
This review is from: Information Visualization in Data Mining and Knowledge Discovery (The Morgan Kaufmann Series in Data Management Systems) (Hardcover)
This book is the result of two workshop whose goals were to open up the dialog between researchers in visualization and data mining, two key areas involved in data exploration. Publication was delayed for many reasons and MK agreed to publish the workshop proceedings.

It was difficult to do provide a historical record (thus all the workshop papers) and at the same time elegant content for future readers. A balance was struck - additional tutorials provided, some organization, and edited papers. The result should be viewed in that context: a collection of papers, some of which are tutorial, some idealized positions, some seminal in nature, and some provocative. These are the works of creative and insighful individuals and I am pleased to see them disseminated.

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Inside This Book (learn more)
First Sentence:
In this brief and intensive chapter we provide a broad overview of the field of data visualization. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
generalization state space, domain generalization graphs, many possible summaries, features this data set, integrating data mining, visual data mining, dimensional stacking, data mining results, active summary, multidimensional visualization, data mining models, survey plot, possible data sets, table lens, data mining algorithms, corresponding summary, massive data sets, model visualization, break detection, data mining methods, visualization system, data exploration, knowledge discovery process, data mining process, visualization environment
Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, Simple Seven, University of Massachusetts, Evidence Visualizer, Menlo Park, Computer Society Press, British Columbia, John Wiley, Proceedings of the Third International Conference, San Francisco, Los Alamitos, National Academy Press, Newport Beach, Computer Science, Computer World, False Play, Morgan Kaufmann, Oak Ridge National Laboratory, Silicon Graphics, Graphics Press, Outliers Clusters Clusters Features Features Rule, The Grand Tour, Academic Press, Case-Building Toolkit, Comparing Models
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