Data Mining - Mehmed Kantardzic [27]
Ratios are the second simple transformation of a target or output features. Using s(t + 1)/s(t) as the output of a data-mining process instead of absolute value s(t + 1) means that the level of increase or decrease in the values of a feature may also improve the performances of the entire mining process.
Differences and ratio transformations are not only useful for output features but also for inputs. They can be used as changes in time for one feature or as a composition of different input features. For example, in many medical data sets, there are two features of a patient (height and weight) that are taken as input parameters for different diagnostic analyses. Many applications show that better diagnostic results are obtained when an initial transformation is performed using a new feature called the body mass index (BMI), which is the weighted ratio between weight and height. This composite feature is better than the initial parameters to describe some of the characteristics of the patient, such as whether or not the patient is overweight.
Logical transformations can also be used to compose new features. For example, sometimes it is useful to generate a new feature that will determine the logical value of the relation A > B between existing features A and B. But there are no universally best data-transformation methods. The lesson to be learned is that a major role remains for human insight while defining the problem. Attention should be paid to composing features, because relatively simple transformations can sometimes be far more effective for the final performance than switching to some other techniques of data mining.
2.4 MISSING DATA
For many real-world applications of data mining, even when there are huge amounts of data, the subset of cases with complete data may be relatively small. Available samples and also future cases may have values missing. Some of the data-mining methods accept missing values and satisfactorily process data to reach a final conclusion. Other methods require that all values be available. An obvious question is whether these missing values can be filled in during data preparation, prior to the application of the data-mining methods. The simplest solution for this problem is the reduction of the data set and the elimination of all samples with missing values. That is possible when large data sets are available, and missing values occur only in a small percentage of samples. If we do not drop the samples with missing values, then we have to find values for them. What are the practical solutions?
First, a data miner, together with the domain expert, can manually examine samples that have no values and enter a reasonable, probable, or expected value, based on a domain experience. The method is straightforward for small numbers of missing values and relatively small data sets. But, if there is no obvious or plausible value for each case, the miner is introducing noise into the data set by manually generating a value.
The second approach gives an even simpler solution for elimination of missing values. It is based on a formal, often automatic replacement of missing values with some constants, such as:
1. replace all missing values with a single global constant (a selection of a global constant is highly application dependent);
2. replace a missing value with its feature mean; and
3. replace a missing value with its feature mean for the given class (this approach is possible only for classification problems where samples are classified in advance).
These simple solutions are tempting. Their main flaw is that the substituted value is not the correct value. By replacing