Constrained Data Mining

Constrained Data Mining

Brad Morantz (Science Applications International Corporation, USA)
Copyright: © 2009 |Pages: 6
DOI: 10.4018/978-1-60566-010-3.ch048
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Mining a large data set can be time consuming, and without constraints, the process could generate sets or rules that are invalid or redundant. Some methods, for example clustering, are effective, but can be extremely time consuming for large data sets. As the set grows in size, the processing time grows exponentially. In other situations, without guidance via constraints, the data mining process might find morsels that have no relevance to the topic or are trivial and hence worthless. The knowledge extracted must be comprehensible to experts in the field. (Pazzani, 1997) With time-ordered data, finding things that are in reverse chronological order might produce an impossible rule. Certain actions always precede others. Some things happen together while others are mutually exclusive. Sometimes there are maximum or minimum values that can not be violated. Must the observation fit all of the requirements or just most. And how many is “most?” Constraints attenuate the amount of output (Hipp & Guntzer, 2002). By doing a first-stage constrained mining, that is, going through the data and finding records that fulfill certain requirements before the next processing stage, time can be saved and the quality of the results improved. The second stage also might contain constraints to further refine the output. Constraints help to focus the search or mining process and attenuate the computational time. This has been empirically proven to improve cluster purity. (Wagstaff & Cardie, 2000)(Hipp & Guntzer, 2002) The theory behind these results is that the constraints help guide the clustering, showing where to connect, and which ones to avoid. The application of user-provided knowledge, in the form of constraints, reduces the hypothesis space and can reduce the processing time and improve the learning quality.
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Data mining has been defined as the process of using historical data to discover regular patterns in order to improve future decisions. (Mitchell, 1999) The goal is to extract usable knowledge from data. (Pazzani, 1997) It is sometimes called knowledge discovery from databases (KDD), machine learning, or advanced data analysis. (Mitchell, 1999)

Due to improvements in technology, the amount of data collected has grown substantially. The quantities are so large that proper mining of a database can be extremely time consuming, if not impossible, or it can generate poor quality answers or muddy or meaningless patterns. Without some guidance, it is similar to the example of a monkey on a typewriter: Every now and then, a real word is created, but the vast majority of the results is totally worthless. Some things just happen at the same time, yet there exists no theory to correlate the two, as in the proverbial case of skirt length and stock prices.

Some of the methods of deriving knowledge from a set of examples are: association rules, decision trees, inductive logic programming, ratio rules, and clustering, as well as the standard statistical procedures. Some also use neural networks for pattern recognition or genetic algorithms (evolutionary computing). Semi-supervised learning, a similar field, combines supervised learning with self-organizing or unsupervised training to gain knowledge (Zhu, 2006) (Chappelle et al., 2006). The similarity is that both constrained data mining and semi-supervised learning utilize the a-priori knowledge to help the overall learning process.

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