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Data reduction is the transformation of numerical or alphabetical digital information derived empirically or experimentally into a corrected, ordered, and simplified form. . The purpose of data reduction can be two-fold: reduce the number of data records by eliminating invalid data or produce summary data and statistics at different aggregation levels for various applications
Instance selection (or dataset reduction, or dataset condensation) is an important data pre-processing step that can be applied in many machine learning (or data mining) tasks. [1] Approaches for instance selection can be applied for reducing the original dataset to a manageable volume, leading to a reduction of the computational resources that ...
Data collection and validation consist of four steps when it involves taking a census and seven steps when it involves sampling. [3] A formal data collection process is necessary, as it ensures that the data gathered are both defined and accurate. This way, subsequent decisions based on arguments embodied in the findings are made using valid ...
The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large ...
Data compression aims to reduce the size of data files, enhancing storage efficiency and speeding up data transmission. K-means clustering, an unsupervised machine learning algorithm, is employed to partition a dataset into a specified number of clusters, k, each represented by the centroid of its points.
In computing, data transformation is the process of converting data from one format or structure into another format or structure. It is a fundamental aspect of most data integration [ 1 ] and data management tasks such as data wrangling , data warehousing , data integration and application integration.
Data wrangling can benefit data mining by removing data that does not benefit the overall set, or is not formatted properly, which will yield better results for the overall data mining process. An example of data mining that is closely related to data wrangling is ignoring data from a set that is not connected to the goal: say there is a data ...
The outer circle in the diagram symbolizes the cyclic nature of data mining itself. A data mining process continues after a solution has been deployed. The lessons learned during the process can trigger new, often more focused business questions, and subsequent data mining processes will benefit from the experiences of previous ones.