Data Mining using SAS Enterprise Miner

I have recently found two new books about data mining. The author of these two books is Randall Matignon. He works at Amgen, Inc. in South San Francisco, California. He is a SAS/Microsoft Office VBA programmer with more than twenty years of experience. His expertise domains include pharmaceutical healthcare and biotechnology industries. Below is a short description of his two books.

Data Mining Using SAS Enterprise Miner

Data Mining Using SAS Enterprise Miner introduces the reader to a wide variety of data mining techniques in SAS® Enterprise Miner. This first-of-a-kind book explains the purpose of -- and reasoning behind -- every node that is a part of SAS® Enterprise Miner with regard to SEMMA design and SAS data mining analysis. Each chapter starts with a short introduction to the assortment of statistics that are generated from the various SAS® Enterprise Miner nodes, followed by detailed explanations of the configuration settings and the generated results that are located within each node. The end result of the author’s meticulous presentation is a well crafted study guide on the various methods that one employs to randomly sample, partition, transform, and filter the data within the process flow of SAS® Enterprise Miner. The book will explain the wide assortment of modeling designs that are available in addition to the process of assessing the various models under comparison in SAS® Enterpris e Miner v4.3.

Neural Network Modeling using SAS Enterprise Miner

Neural Network Modeling using SAS Enterprise Miner introduces the readers to a non-linear modeling design called neural network modeling using SAS Enterprise Miner. The book will also familiarize the readers with this predictive and classification methodology in statistics called neural network modeling. This book is designed in making statisticians, researchers and programmers aware of the awesome new product now available in SAS® called Enterprise Miner. This first of its kind book will reveal the strength and ease of use of the powerful new module in SAS® with step-by-step instructions in20creating a process flow diagram in preparation to data mining analysis and neural network predictive and classification modeling using SAS® Enterprise Miner v4.3.

For more information, visit www.sasenterpriseminer.com

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The numbers behind Numb3rs

Do you watch Numb3rs? It is a TV show where the two main characters are a FBI agent (Don) and his younger brother (Charlie), a very clever mathematician. The show is based on the idea that crimes can be solved with numbers. In each episode, Charlie uses different mathematic techniques to find information regarding some crime scene. In an episode of the third season (Brutus), Charlie uses data mining techniques.

To be honest, I haven't watched this episode. I'm not a big fan of the show, but I find it interesting sometimes. If, like me, you missed this episode, Devlin and Lorden have written a book about the show: The Numbers Behind Numb3rs: Solving Crime with Mathematics (Plume Book, 2007).

This book describes the mathematics behind Numb3rs. Chapter 3 (on data mining) is quite interesting. Authors are mentioning facts and anecdotes relevant to data mining. However, it is often not related to the show itself (it goes deeper in explaining the details of some methods). The book tries to vulgarize the concepts of data mining. It is quite normal since the audience certainly consists of people interested by the show.

The problem is that sometimes, the text is so vulgarized that it is wrong. Look at the quote below, taken from the page 28 of the book:

"Neural networks: special kinds of computer programs that can predict the probability of crimes and terrorist attacks."

Oops! This is not vulgarization anymore. It is simply wrong. This is the bigger risk of vulgarization: trying to change the terms to make an explanation clearer may ends up incorrect. However, the rest of the chapter is generally fine and even sometimes very technical.

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SVM, neural network and decision tree

After reading a post concerning the PAKDD 2007 competition on Abbott's Analytics, I was curious about the trends of some data mining methods. I decided to play with Google Trends using three common methods: Support Vector Machine (SVM), Artificial Neural Network (ANN) and Decision Tree (DT). The following picture shows the trends in search on Google for the three terms "svm", "neural network" and "decision tree" since 2004:

Red = "neural network", blue = "svm", orange = "decision tree"
The main observation is that SVM and ANN seem to be less trendy these last years. It is interesting to see that DT are constant over the years. These are the first conclusions we could draw from this picture. However, it is always dangerous to conclude on some numbers. In the above case, several factors have to be taken into account when making such conclusions:

• The way of writing the searched terms. For example, SVM could be found under "support vector machine", "support vector", "svm", etc. However, it seems that "svm" is most often used. The same remark for neural networks is also valid.
  
  
• The diversity of search engines. Although the most popular, Google is not the only search engine on the web. A lot of people may use other engines such as Yahoo!, Live Search or All the Web. Only searches on Google are considered in this picture.
  
  
• The difference between "searching" and "using". In other words, people may search for some methods but finally decide to use another one. Therefore, the fact that a keyword is often searched on Google does not mean that the corresponding method is used.

Consequently, even if these kind of plots look nice, interpreting the information they give and in which context it is valid is not an easy task.

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Data mining application: neural networks on seismic data

Today, we continue our worldwide discovery of applications of data mining in a specific engineering domain. I'm sure you know a lot of applications of neural networks to face, speech or digit recognition. The paper by Huang et al. (1) is a good example of neural network's application in an original area: diagnosis of a building using seismic response data. They present a procedure for diagnosing if a building has been damaged by earthquakes. A backpropagation neural network is used to compare buildings after a small earthquake (without damage) with data from damaged buildings (after big earthquakes).

(1) Huang C.S., Hung S.L., Wen C.M. and Tu T.T., A neural network approach for structural identification and diagnosis of a building from seismic response data, Earthquake engineering and structural dynamics, 32, 2003, pp 187-206.

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