Information Visualization in Data Mining and Knowledge Discovery (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.

This is a book about a new field circling while getting a sense of direction. We very seldom get such an honest look into just how this sort of process happens. Instead, we get the bottom line version where everything obviously inevitably followed from what came before to the predestined conclusion -- in 20-20 hindsight.

5 Stars for this one


Editor's note   October 17, 2001
 5 out of 7 found this review helpful

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.


Some good work but the editing could use some work   September 11, 2001
 7 out of 8 found this review helpful

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.


Did the editors ever look at this?   September 5, 2001
 22 out of 26 found this review helpful

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?