Data Mining Solutions: Methods and Tools for Solving Real-World Problems

Rating:
Summary: A practical resource to read before you start data mining.
Review: Data mining is an important segment of computer science that is growing in popularity. It is also diverging into many different branches. As a developer of shareware that targets quantitative data mining solutions, I purchased this book to improve my understanding of the full data mining market. This book helped me understand where our software fits into the bigger picture and more importantly the detail in the book has provided us with some worthwhile new ways to improve our software. Additionally, it is the only book available that covers the spectrum of visual data mining methods and systems.

For the ordinary person, a book such as this is a good place to start data mining. To achieve anything useful with data mining, you first need to get an idea of what you are trying to achieve and how you are going to achieve it. Without this background knowledge you are more than likely going to waste a lot of valuable time and quite a lot of money on the expensive tools that service the market.

Data Mining Solutions will help you get on the right path as it has a good blend of academic detail, software overviews and most importantly it gives practical details as to why you might want to use data mining in the first place. There are also some quite interesting insights into issues like fraud, which add some drama to the computer-related discussions.

The usual approach taken in computing is to first purchase the product and then learn the science. For data mining, this purchase should be preceded by some practical background reading from a resource such as this. I highly recommend this book as a place to start your voyages of data mining discovery.

Rating:
Summary: A good introduction to data mining
Review: I think this is one of the best books on data mining. The book covers whole range of issues related to data mining and it makes the book in a way unique since you will find it extremely difficult to find another source that is as comprehensive as Data Mining Solutions. Although data mining is a quite complicated process, the book is easy to read. Authors maintain nice flow of ideas through out the book. Now the strong and weak things about the book will follow.

Things I particularly liked in the book:

· Excellent introduction to the topic. I have tried other sources on data mining and this book is by far the most comprehensive and easy to understand.

· An extremely good blend of theory and practice. Authors seem to be very experienced in dealing with various data mining issues. Solutions and examples from telecommunications, fraud detection and other areas show how the theoretical background can be applied to real life problems.

· Authors cover not only the processes of data preparation and analysis but also explain the ways of presenting the results which I think is crucial thing to one's success.

· Authors try not to over-emphasize the power of various data mining techniques. You will find explanations of pros & cons for each of the techniques and suggestions on when to use them with the most success.

· The book includes a good coverage of data mining tools (software) that will help you once you decide to apply data mining techniques to real world problems.

Things that you might miss:

· Sometimes figures lack description so it takes some time to understand what authors have meant.

· Sometimes you might want deeper description of topics covered.

Overall, I think this is a really good book on data mining, especially for newcomers. The book gives a broad understanding of various data mining areas and it definitely can serve as a good starting point for gaining more benefit out of the existing databases in your business. I give five stars out of five.

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Summary: Visual Data Mining - A new way to look for patterns & trends
Review: In creating this book, we felt it important to cover an area of data mining that is quickly becoming the preferred method of choice for discovering patterns and trends - visualization. The use of visualization provides benefits that support fast training, rapid application development, excellent pattern detection, and most importantly a quick return on investment (ROI). Many other data mining approaches can't make these types of claims. Our experiences speak for themselves.

This is a book about visual data mining techniques and technologies. Although the topics are briefly discussed and generally useful, this is not a book about statistical testing, information theory, artificial intelligence, or non-visual data mining algorithms (e.g., neural networks, decision trees, unsupervised learning, etc). Each section of the book was designed to convey information pertaining to the use of visual data mining. Most of the discussions are derived from real-world experiences, so the descriptions are based on true and actual accounts of performing the work, rather than theoretical discussions or complex mathematical proofs. This means that you can apply these techniques to your own domains. There is even a section with comprehensive product descriptions that you can reference to get an understanding of the current state of visual data mining systems.

If anything, the book will give you a different perspective on the world of data mining. The topics are new, refreshing, and unlike anything else that has ever been written on the field of data mining. We also invite you to visit our homepage to view more about this book and to learn more on the topic of visual data mining.

Rating:
Summary: Innovative practical guide to visual data mining methods
Review: This practical guide to visual data mining strategies for networks, hierarchies, temporal data, and landscapes is the best I have seen. The realistic examples from financial fraud detection, telecommunications network analysis, retail sales, bio-informatics, etc. reveal the remarkable power of these novel techniques. The authors guide readers through the process of data preparation, analysis, and discovery with numerous figures from many of the rapidly emerging commercial systems. If you have important data that you must understand, you'll benefit from this book.

Rating:
Summary: Innovative practical guide to visual data mining methods
Review: This practical guide to visual data mining strategies for networks, hierarchies, temporal data, and landscapes is the best I have seen. The realistic examples from financial fraud detection, telecommunications network analysis, retail sales, bio-informatics, etc. reveal the remarkable power of these novel techniques. The authors guide readers through the process of data preparation, analysis, and discovery with numerous figures from many of the rapidly emerging commercial systems. If you have important data that you must understand, you'll benefit from this book.

Rating:
Summary: A good introduction to data mining
Review: Westphal and Blaxton do a good job of introducing data mining concepts, but focus too heavily on data visualization techniques. A large chunk of the book is devoted to tool walk throughs, information that will be outdated soon. The case studies cover several industries, but fail to show the authors' methodology in action. The greatest shortcoming is the EXTREMELY brief discussion on neural networks, genetic algorithms and other non-visual analytic methods. If you are a beginner in data visualization, buy this book. Anyone looking for in depth (or even an intro) discussion on AI and statistics, look elsewhere.

Rating:
Summary: Good intro to data visualization for novices.
Review: Westphal and Blaxton do a good job of introducing data mining concepts, but focus too heavily on data visualization techniques. A large chunk of the book is devoted to tool walk throughs, information that will be outdated soon. The case studies cover several industries, but fail to show the authors' methodology in action. The greatest shortcoming is the EXTREMELY brief discussion on neural networks, genetic algorithms and other non-visual analytic methods. If you are a beginner in data visualization, buy this book. Anyone looking for in depth (or even an intro) discussion on AI and statistics, look elsewhere.