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Semantic data mining is a subset of data mining that specifically seeks to incorporate domain knowledge, such as formal semantics, into the data mining process.Domain knowledge is the knowledge of the environment the data was processed in. Domain knowledge can have a positive influence on many aspects of data mining, such as filtering out redundant or inconsistent data during the preprocessing ...
Given the variety of data sources (e.g. databases, business applications) that provide data and formats that data can arrive in, data preparation can be quite involved and complex. There are many tools and technologies [5] that are used for data preparation. The cost of cleaning the data should always be balanced against the value of the ...
Data understanding; Data preparation; Modeling; Evaluation; Deployment; or a simplified process such as (1) Pre-processing, (2) Data Mining, and (3) Results Validation. Polls conducted in 2002, 2004, 2007 and 2014 show that the CRISP-DM methodology is the leading methodology used by data miners. [15] [16] [17] [18]
Data cleansing may also involve harmonization (or normalization) of data, which is the process of bringing together data of "varying file formats, naming conventions, and columns", [2] and transforming it into one cohesive data set; a simple example is the expansion of abbreviations ("st, rd, etc." to "street, road, etcetera").
To make the data amenable for machine learning, an expert may have to apply appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods. After these steps, practitioners must then perform algorithm selection and hyperparameter optimization to maximize the predictive performance of their model.
Feature standardization makes the values of each feature in the data have zero-mean (when subtracting the mean in the numerator) and unit-variance. This method is widely used for normalization in many machine learning algorithms (e.g., support vector machines , logistic regression , and artificial neural networks ).
Most preprocessors are specific to a particular data processing task (e.g., compiling the C language). A preprocessor may be promoted as being general purpose , meaning that it is not aimed at a specific usage or programming language, and is intended to be used for a wide variety of text processing tasks.
Code generation is the process of generating executable code (e.g. SQL, Python, R, or other executable instructions) that will transform the data based on the desired and defined data mapping rules. [4] Typically, the data transformation technologies generate this code [5] based on the definitions or metadata defined by the developers.