Mining health records

Yet another battle between companies mining personal data and privacy advocates is ongoing. Microsoft is playing the bad in this story where the personal data consist of health records. According to Government Health IT, Microsoft plans to develop "a clinical data warehouse (CDW) that provides predefined queries of interest to clinicians and analysts". The next step is to apply data mining techniques such as clustering or supervised learning to find unknown trends in data.

The Patient Privacy Rights Foundation warns against the fact that data could be used as "goldmine ripe for exploitation" and rather suggest data to be "only accessed with express consent by the patient". This is a good advice that will certainly be difficult to guarantee.

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Data mining for the car industry

The Auto Industry website has an article of a few lines summarizing a data mining application for the car industry. More precisely, Ford is using data mining for early warning of supplier failure. After North American suppliers, they plan to cover Europe and Latin America. An example of application is to track late shipment events to be able to minimize this problem.

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Data mining application: terrorism

Here is another example, certainly well known, of what data mining can do. Colleen McCue has written an interesting paper about this topic in the Defense Intelligence Journal. This article has the advantage to be understandable by people interested in data mining but not familiar with the topic of terrorism.

As written in the paper, the environment is saturated by information. The article shows why it is the case when dealing with terrorism data. Fayyad et al. (1) had already written that "The capacity of digital data storage worldwide has doubled every nine months for at least a decade, at twice the rate predicted by Moore's Law for the growth of computing power during the same period". It seems to be particularly true for terrorism data.

The paper covers areas such as information collection, identity theft and anomaly detection (among others) in a predictive analysis view. It is a comprehensive paper giving a good idea of nowadays applications in data mining against terrorism. When defining terrorism, the author writes "[...] terrorism can be described as violence with a larger agenda". Although this definition is understandable, a more precise description of what exactly terrorism is would have been a plus.

If, like me, you want to know more about data mining and predictive analytics against terrorism, I suggest you the new book from McCue: Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis.

(1) Usama M. Fayyad, Ramasamy Uthurusamy: Evolving data into mining solutions for insights. Commun. ACM 45(8): 28-31 (2002)

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Detecting fake Van Gogh

Here is yet another exciting data mining application: detecting fake paintings. According to NewScientist, researchers from the Maastricht University (Netherlands) are using data mining techniques to discover whether paintings are original or not. For example, they train a neural network on some Van Gogh's painting to discover the trends let by its famous author. Even if this technique seems promising, the human intervention cannot be avoided. The human is still involved in the process of the training set establishment. Indeed, a human expert has once to say to the machine which painting really is an authentic one.

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Mining crime information

Using data mining techniques to help fighting crime sounds good. It is certainly an interesting topic. Can you imagine saying that you have caught a criminal using a decision tree? :-) Although this view is very simplistic, data mining seems to be helpful in some situations as pointed out by LocalTechWire. The main idea is to use data mining methods to identify crime trends and then anticipate crimes. This is certainly a trendy topic for data mining, proved by the publication of a new book by Colleen McCue: Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis.

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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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Data mining application: oil production prediction

One purpose of this blog is to give pointers to interesting research in data mining. More particularly, I want to emphasis on data mining applications to non usual domains such as engineering. An example of research field for data mining is explained in the work of Nguyen and Chan in 2005 (1). Their paper describes a decision support system which uses curve fitting and neural networks. The goal is to help the user in predicting oil production.

(1) Nguyen H.H. and Chan C.W., Applications of data analysis techniques for oil production prediction, Engineering Applications of Artificial Intelligence, 18, 2005, pp 549-558.

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