Data Mining - Mehmed Kantardzic [54]
One possible classification of sampling methods in data mining is based on the scope of application of these methods, and the main classes are
1. general-purpose sampling methods
2. sampling methods for specific domains.
In this text we will introduce only some of the techniques that belong to the first class because they do not require specific knowledge about the application domain and may be used for a variety of data-mining applications.
Systematic sampling is the simplest sampling technique. For example, if we want to select 50% of a data set, we could take every other sample in a database. This approach is adequate for many applications and it is a part of many data-mining tools. However, it can also lead to unpredicted problems when there are some regularities in the database. Therefore, the data miner has to be very careful in applying this sampling technique.
Random sampling is a method by which every sample from an initial data set has the same chance of being selected in the subset. The method has two variants: random sampling without replacement and random sampling with replacement. Random sampling without replacement is a popular technique in which n distinct samples are selected from N initial samples in the data set without repetition (a sample may not occur twice). The advantages of the approach are simplicity of the algorithm and nonexistence of any bias in a selection. In random sampling with replacement, the samples are selected from a data set such that all samples are given an equal chance of being selected, no matter how often they already have been drawn, that is, any of the samples may be selected more than once. Random sampling is not a one-time activity in a data-mining process. It is an iterative process, resulting in several randomly selected subsets of samples. The two basic forms of a random sampling process are as follows.
1. Incremental Sampling. Mining incrementally larger random subsets of samples that have many real-world applications helps spot trends in error and complexity. Experience has shown that the performance of the solution may level off rapidly after some percentage of the available samples has been examined. A principal approach to case reduction is to perform a data-mining process on increasingly larger random subsets, to observe the trends in performances, and to stop when no progress is made. The subsets should take big increments in data sets, so that the expectation of improving performance with more data is reasonable. A typical pattern of incremental subsets might be 10, 20, 33, 50, 67, and 100%. These percentages are reasonable, but can be adjusted based on knowledge of the application and the number of samples in the data set. The smallest subset should be substantial, typically, no fewer than 1000 samples.
2. Average Sampling. When the solutions found from many random subsets of samples of cases are averaged or voted, the combined solution can do as well or even better than the single solution found on the full collection of data. The price of this approach is the repetitive process of data mining on smaller sets of samples and, additionally, a heuristic definition of criteria to compare the several solutions of different subsets of data. Typically, the process of voting between solutions is applied for classification problems (if three solutions are class1 and one solution is class2, then the final voted solution is class1) and averaging for regression problems (if