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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 ...
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").
A common source for data is a data mart or data warehouse. Pre-processing is essential to analyze the multivariate data sets before data mining. The target set is then cleaned. Data cleaning removes the observations containing noise and those with missing data.
For example, if you need to load data into two databases, you can run the loads in parallel (instead of loading into the first – and then replicating into the second). Sometimes processing must take place sequentially. For example, dimensional (reference) data are needed before one can get and validate the rows for main "fact" tables.
Data preparation is the first step in data analytics projects and can include many discrete tasks such as loading data or data ingestion, data fusion, data cleaning, data augmentation, and data delivery. [2] The issues to be dealt with fall into two main categories:
Data sanitization methods are also applied for the cleaning of sensitive data, such as through heuristic-based methods, machine-learning based methods, and k-source anonymity. [ 2 ] This erasure is necessary as an increasing amount of data is moving to online storage, which poses a privacy risk in the situation that the device is resold to ...
A common example from computer programming is the processing performed on source code before the next step of compilation. In some computer languages (e.g., C and PL/I) there is a phase of translation known as preprocessing. It can also include macro processing, file inclusion and language extensions.
tsflex is an open source Python library for extracting features from time series data. [27] Despite being 100% written in Python, it has been shown to be faster and more memory efficient than tsfresh, seglearn or tsfel. [28] seglearn is an extension for multivariate, sequential time series data to the scikit-learn Python library. [29]