Table of contents:
introduction to data miningp
Simple data mining examples and datasets
Fielded applications of data mining and machine learning
The difference between machine learning and statistics in data mining
Information and examples on data mining and ethics
Data acquisition and integration techniques
What is a data rollup?
Calculating mode in data mining projects
Using data merging and concatenation techniques to integrate data
1.3 Fielded Applications
The examples that we opened with are speculative research projects, not production systems. And the preceding illustrations are toy problems: they are deliberately chosen to be small so that we can use them to work through algorithms later in the book. Where's the beef? Here are some applications of machine learning that have actually been put into use.
Because they are fielded applications, the illustrations that follow tend to stress the use of learning in performance situations, in which the emphasis is on ability to perform well on new examples. This book also describes the use of learning systems to gain knowledge from decision structures that are inferred from the data. We believe that this is as important — probably even more important in the long run — a use of the technology as merely making high-performance predictions. Still, it will tend to be underrepresented in fielded applications because when learning techniques are used to gain insight, the result is not normally a system that is put to work as an application in its own right. Nevertheless, in three of the examples that follow, the fact that the decision structure is comprehensible is a key feature in the successful adoption of the application.
1.3.1 Decisions Involving Judgment
When you apply for a loan, you have to fill out a questionnaire that asks for relevant financial and personal information. The loan company uses this information as the basis for its decision as to whether to lend you money. Such decisions are typically made in two stages. First, statistical methods are used to determine clear "accept" and "reject" cases. The remaining borderline cases are more difficult and call for human judgment. For example, one loan company uses a statistical decision procedure to calculate a numeric parameter based on the information supplied in the questionnaire. Applicants are accepted if this parameter exceeds a preset threshold and rejected if it falls below a second threshold. This accounts for 90 percent of cases, and the remaining 10 percent are referred to loan officers for a decision. On examining historical data on whether applicants did indeed repay their loans, however, it turned out that half of the borderline applicants who were granted loans actually defaulted. Although it would be tempting simply to deny credit to borderline customers, credit industry professionals pointed out that if only their repayment future could be reliably determined it is precisely these customers whose business should be wooed; they tend to be active customers of a credit institution because their finances remain in a chronically volatile condition. A suitable compromise must be reached between the viewpoint of a company accountant, who dislikes bad debt, and that of a sales executive, who dislikes turning business away.
Enter machine learning. The input was 1000 training examples of borderline cases for which a loan had been made that specified whether the borrower had finally paid off or defaulted. For each training example, about 20 attributes were extracted from the questionnaire, such as age, years with current employer, years at current address, years with the bank, and other credit cards possessed. A machine learning procedure was used to produce a small set of classification rules that made correct predictions on two-thirds of the borderline cases in an independently chosen test set. Not only did these rules improve the success rate of the loan decisions, but the company also found them attractive because they could be used to explain to applicants the reasons behind the decision. Although the project was an exploratory one that took only a small development effort, the loan company was apparently so pleased with the result that the rules were put into use immediately.
1.3.2 Screening Images
Since the early days of satellite technology, environmental scientists have been trying to detect oil slicks from satellite images to give early warning of ecologic disasters and deter illegal dumping. Radar satellites provide an opportunity for monitoring coastal waters day and night, regardless of weather conditions. Oil slicks appear as dark regions in the image whose size and shape evolve depending on weather and sea conditions. However, other look-alike dark regions can be caused by local weather conditions such as high wind. Detecting oil slicks is an expensive manual process requiring highly trained personnel who assess each region in the image.
A hazard detection system has been developed to screen images for subsequent manual processing. Intended to be marketed worldwide to a wide variety of users — government agencies and companies — with different objectives, applications, and geographic areas, it needs to be highly customizable to individual circumstances. Machine learning allows the system to be trained on examples of spills and nonspills supplied by the user and lets the user control the trade-off between undetected spills and false alarms. Unlike other machine learning applications, which generate a classifier that is then deployed in the field, here it is the learning method itself that will be deployed.
The input is a set of raw pixel images from a radar satellite, and the output is a much smaller set of images with putative oil slicks marked by a colored border. First, standard image processing operations are applied to normalize the image. Then, suspicious dark regions are identified. Several dozen attributes are extracted from each region, characterizing its size, shape, area, intensity, sharpness and jaggedness of the boundaries, proximity to other regions, and information about the background in the vicinity of the region. Finally, standard learning techniques are applied to the resulting attribute vectors.
Several interesting problems were encountered. One is the scarcity of training data. Oil slicks are (fortunately) very rare, and manual classification is extremely costly. Another is the unbalanced nature of the problem: of the many dark regions in the training data, only a small fraction are actual oil slicks. A third is that the examples group naturally into batches, with regions drawn from each image forming a single batch, and background characteristics vary from one batch to another. Finally, the performance task is to serve as a filter, and the user must be provided with a convenient means of varying the false-alarm rate.
1.3.3 Load Forecasting
In the electricity supply industry, it is important to determine future demand for power as far in advance as possible. If accurate estimates can be made for the maximum and minimum load for each hour, day, month, season, and year, utility companies can make significant economies in areas such as setting the operating reserve, maintenance scheduling, and fuel inventory management.
An automated load forecasting assistant has been operating at a major utility supplier over the past decade to generate hourly forecasts 2 days in advance. The first step was to use data collected over the previous 15 years to create a sophisticated load model manually. This model had three components: base load for the year, load periodicity over the year, and the effect of holidays. To normalize for the base load, the data for each previous year was standardized by subtracting the average load for that year from each hourly reading and dividing by the standard deviation over the year. Electric load shows periodicity at three fundamental frequencies: diurnal, where usage has an early morning minimum and midday and afternoon maxima; weekly, where demand is lower at weekends; and seasonal, where increased demand during winter and summer for heating and cooling, respectively, creates a yearly cycle. Major holidays such as Thanksgiving, Christmas, and New Year' s Day show significant variation from the normal load and are each modeled separately by averaging hourly loads for that day over the past 15 years. Minor official holidays, such as Columbus Day, are lumped together as school holidays and treated as an offset to the normal diurnal pattern. All of these effects are incorporated by reconstructing a year's load as a sequence of typical days, fitting the holidays in their correct position, and denormalizing the load to account for overall growth.
Thus far, the load model is a static one, constructed manually from historical data, and implicitly assumes "normal" climatic conditions over the year. The final step was to take weather conditions into account using a technique that locates the previous day most similar to the current circumstances and uses the historical information from that day as a predictor. In this case the prediction is treated as an additive correction to the static load model. To guard against outliers, the 8 most similar days are located and their additive corrections averaged. A database was constructed of temperature, humidity, wind speed, and cloud cover at three local weather centers for each hour of the 15-year historical record, along with the difference between the actual load and that predicted by the static model. A linear regression analysis was performed to determine the relative effects of these parameters on load, and the coefficients were used to weight the distance function used to locate the most similar days.
The resulting system yielded the same performance as trained human forecasters but was far quicker — taking seconds rather than hours to generate a daily forecast. Human operators can analyze the forecast's sensitivity to simulated changes in weather and bring up for examination the "most similar" days that the system used for weather adjustment.
Diagnosis is one of the principal application areas of expert systems. Although the handcrafted rules used in expert systems often perform well, machine learning can be useful in situations in which producing rules manually is too labor intensive.
Preventative maintenance of electromechanical devices such as motors and generators can forestall failures that disrupt industrial processes. Technicians regularly inspect each device, measuring vibrations at various points to determine whether the device needs servicing. Typical faults include shaft misalignment, mechanical loosening, faulty bearings, and unbalanced pumps. A particular chemical plant uses more than 1000 different devices, ranging from small pumps to very large turbo-alternators, which until recently were diagnosed by a human expert with 20 years of experience. Faults are identified by measuring vibrations at different places on the device's mounting and using Fourier analysis to check the energy present in three different directions at each harmonic of the basic rotation speed. The expert studies this information, which is noisy because of limitations in the measurement and recording procedure, to arrive at a diagnosis. Although handcrafted expert system rules had been developed for some situations, the elicitation process would have to be repeated several times for different types of machinery; so a learning approach was investigated.
Six hundred faults, each comprising a set of measurements along with the expert's diagnosis, were available, representing 20 years of experience in the field. About half were unsatisfactory for various reasons and had to be discarded; the remainder were used as training examples. The goal was not to determine whether or not a fault existed but to diagnose the kind of fault, given that one was there. Thus, there was no need to include fault-free cases in the training set. The measured attributes were rather low level and had to be augmented by intermediate concepts, that is, functions of basic attributes, which were defined in consultation with the expert and embodied some causal domain knowledge. The derived attributes were run through an induction algorithm to produce a set of diagnostic rules. Initially, the expert was not satisfied with the rules because he could not relate them to his own knowledge and experience. For him, mere statistical evidence was not, by itself, an adequate explanation. Further background knowledge had to be used before satisfactory rules were generated. Although the resulting rules were complex, the expert liked them because he could justify them in light of his mechanical knowledge. He was pleased that a third of the rules coincided with ones he used himself and was delighted to gain new insight from some of the others.
Performance tests indicated that the learned rules were slightly superior to the handcrafted ones that the expert had previously elicited, and subsequent use in the chemical factory confirmed this result. It is interesting to note, however, that the system was put into use not because of its good performance but because the domain expert approved of the rules that had been learned.
1.3.5 Marketing and Sales
Some of the most active applications of data mining have been in the area of marketing and sales. These are domains in which companies possess massive volumes of precisely recorded data, data that — it has only recently been realized — is potentially extremely valuable. In these applications, predictions themselves are the chief interest: the structure of how decisions are made is often completely irrelevant.
We have already mentioned the problem of fickle customer loyalty and the challenge of detecting customers who are likely to defect so that they can be wooed back into the fold by giving them special treatment. Banks were early adopters of data mining technology because of their successes in the use of machine learning for credit assessment. Data mining is now being used to reduce customer attrition by detecting changes in individual banking patterns that may herald a change of bank or even life changes, such as a move to another city, that could result in a different bank being chosen. It may reveal, for example, a group of customers with above-average attrition rate who do most of their banking by phone after hours when telephone response is slow. Data mining may determine groups for whom new services are appropriate, such as a cluster of profitable, reliable customers who rarely get cash advances from their credit card except in November and December, when they are prepared to pay exorbitant interest rates to see them through the holiday season. In another domain, cellular phone companies fight churn by detecting patterns of behavior that could benefit from new services and then advertise such services to retain their customer base. Incentives provided specifically to retain existing customers can be expensive, and successful data mining allows them to be precisely targeted to those customers where they are likely to yield maximum benefit.
Market basket analysis is the use of association techniques to find groups of items that tend to occur together in transactions, typically supermarket checkout data. For many retailers, this is the only source of sales information that is available for data mining. For example, automated analysis of checkout data may uncover the fact that customers who buy beer also buy chips, a discovery that could be significant from the supermarket operator's point of view (although rather an obvious one that probably does not need a data mining exercise to discover). Or it may come up with the fact that on Thursdays, customers often purchase diapers and beer together, an initially surprising result that, on reflection, makes some sense as young parents stock up for a weekend at home. Such information could be used for many purposes: planning store layouts, limiting special discounts to just one of a set of items that tend to be purchased together, offering coupons for a matching product when one of them is sold alone, and so on. There is enormous added value in being able to identify individual customer's sales histories. In fact, this value is leading to a proliferation of discount cards or "loyalty" cards that allow retailers to identify individual customers whenever they make a purchase; the personal data that results will be far more valuable than the cash value of the discount. Identification of individual customers not only allows historical analysis of purchasing patterns but also permits precisely targeted special offers to be mailed out to prospective customers.
This brings us to direct marketing, another popular domain for data mining. Promotional offers are expensive and have an extremely low — but highly profitable — response rate. Any technique that allows a promotional mailout to be more tightly focused, achieving the same or nearly the same response from a much smaller sample, is valuable. Commercially available databases containing demographic information based on ZIP codes that characterize the associated neighborhood can be correlated with information on existing customers to find a socioeconomic model that predicts what kind of people will turn out to be actual customers. This model can then be used on information gained in response to an initial mailout, where people send back a response card or call an 800 number for more information, to predict likely future customers. Direct mail companies have the advantage over shopping mall retailers of having complete purchasing histories for each individual customer and can use data mining to determine those likely to respond to special offers. Targeted campaigns are cheaper than massmarketed campaigns because companies save money by sending offers only to those likely to want the product. Machine learning can help companies to find the targets.
1.3.6 Other Applications
There are countless other applications of machine learning. We briefly mention a few more areas to illustrate the breadth of what has been done.
Sophisticated manufacturing processes often involve tweaking control parameters. Separating crude oil from natural gas is an essential prerequisite to oil refinement, and controlling the separation process is a tricky job. British Petroleum used machine learning to create rules for setting the parameters. This now takes just 10 minutes, whereas previously human experts took more than a day. Westinghouse faced problems in its process for manufacturing nuclear fuel pellets and used machine learning to create rules to control the process. This was reported to save the company more than $10 million per year (in 1984). The Tennessee printing company R. R. Donnelley applied the same idea to control rotogravure printing presses to reduce artifacts caused by inappropriate parameter settings, reducing the number of artifacts from more than 500 each year to fewer than 30.
In the realm of customer support and service, we have already described adjudicating loans and marketing and sales applications. Another example arises when a customer reports a telephone problem and the company must decide what kind of technician to assign to the job. An expert system developed by Bell Atlantic in 1991 to make this decision was replaced in 1999 by a set of rules acquired using machine learning, which saved more than $10 million per year by making fewer incorrect decisions.
There are many scientific applications. In biology, machine learning is used to help identify the thousands of genes within each new genome. In biomedicine, it is used to predict drug activity by analyzing not just the chemical properties of drugs but also their three-dimensional structure. This accelerates drug discovery and reduces its cost. In astronomy, machine learning has been used to develop a fully automatic cataloging system for celestial objects that are too faint to be seen by visual inspection. In chemistry, it has been used to predict the structure of certain organic compounds from magnetic resonance spectra. In all these applications, machine learning techniques have attained levels of performance — or should we say skill? — that rival or surpass human experts.
Automation is especially welcome in situations involving continuous monitoring, a job that is time consuming and exceptionally tedious for humans. Ecologic applications include the oil spill monitoring described earlier. Some other applications are rather less consequential — for example, machine learning is being used to predict preferences for TV programs based on past choices and advise viewers about the available channels. Still others may save lives. Intensive care patients may be monitored to detect changes in variables that cannot be explained by circadian rhythm, medication, and so on, raising an alarm when appropriate. Finally, in a world that relies on vulnerable networked computer systems and is increasingly concerned about cyber security, machine learning is used to detect intrusion by recognizing unusual patterns of operation.
More on data mining:
- Continue to the next section: The
difference between machine learning and statistics in data mining
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This was first published in May 2009