Data Mining Process:
Data Mining is a process of discovering various models, summaries, and derived values from a given collection of data.
The general experimental procedure adapted to data-mining problems involves the following steps:
1. State the problem and formulate the hypothesis
Most data-based modeling studies are performed in a particular application domain. Hence, domain-specific knowledge and experience are usually necessary to come up with a meaningful problem statement. Unfortunately, many application studies tend to focus on the data-mining technique at the expense of a clear problem statement. In this step, a modeler usually specifies a set of variables for the unknown dependency and, if possible, a general form of this dependency as an initial hypothesis. There may be several hypotheses formulated for a single problem at this stage. The first step requires the combined expertise of an application domain and a data-mining model. In practice, it usually means a close interaction between the data-mining expert and the application expert. In successful data-mining applications, this cooperation does not stop in the initial phase; it continues during the entire data-mining process.
2. Collect the data
This step is concerned with how the data are generated and collected. In general, there are two distinct possibilities. The first is when the data-generation process is under the control of an expert (modeler): this approach is known as a designed experiment. The second possibility is when the expert cannot influence the data-generation process: this is known as the observational approach. An observational setting, namely, random data generation, is assumed in most data-mining applications. Typically, the sampling distribution is completely unknown after data are collected, or it is partially and implicitly given in the data-collection procedure. It is very important, however, to understand how data collection affects its theoretical distribution since such a priori knowledge can be very useful for modeling and, later, for the final interpretation of results. Also, it is important to make sure that the data used for estimating a model and the data used later for testing and applying a model come from the same, unknown, sampling distribution. If this is not the case, the estimated model cannot be successfully used in the final application of the results.
3. Preprocessing the data
In the observational setting, data are usually “collected” from existing databases, data warehouses, and data marts. Data preprocessing usually includes at least two common tasks:
- Outlier detection (and removal) – Outliers are unusual data values that are not consistent with most observations. Commonly, outliers result from measurement errors, coding, and recording errors, and, sometimes, are natural, abnormal values. Such no representative samples can seriously affect the model produced later. There are two strategies for dealing with outliers:
- Detect and eventually remove outliers as a part of the preprocessing phase, or
- Develop robust modeling methods that are insensitive to outliers.
- Scaling, encoding, and selecting features – Data preprocessing includes several steps such as variable scaling and different types of encoding. For example, one feature with the range [0, 1] and the other with the range [−100, 1000] will not have the same weights in the applied technique; they will also influence the final data-mining results differently. Therefore, it is recommended to scale them and bring both features to the same weight for further analysis. Also, application-specific encoding methods usually achieve dimensionality reduction by providing a smaller number of informative features for subsequent data modeling.
These two classes of preprocessing tasks are only illustrative examples of a large spectrum of preprocessing activities in a data-mining process.
Data-preprocessing steps should not be considered completely independent from other data-mining phases. In every iteration of the data-mining process, all activities, together, could define new and improved data sets for subsequent iterations. Generally, a good preprocessing method provides an optimal representation for a data-mining technique by incorporating a priori knowledge in the form of application-specific scaling and encoding.
4. Estimate the model
The selection and implementation of the appropriate data-mining technique is the main task in this phase. This process is not straightforward; usually, in practice, the implementation is based on several models, and selecting the best one is an additional task.
5. Interpret the model and draw conclusions
In most cases, data-mining models should help in decision-making. Hence, such models need to be interpretable in order to be useful because humans are not likely to base their decisions on complex “black-box” models. Note that the goals of the accuracy of the model and the accuracy of its interpretation are somewhat contradictory. Usually, simple models are more interpretable, but they are also less accurate. Modern data-mining methods are expected to yield highly accurate results using high-dimensional models. The problem of interpreting these models, also very important, is considered a separate task, with specific techniques to validate the results. A user does not want hundreds of pages of numeric results. He does not understand them; he cannot summarize, interpret, and use them for successful decision-making.