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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 ...
Rough set-based data analysis methods have been successfully applied in bioinformatics, economics and finance, medicine, multimedia, web and text mining, signal and image processing, software engineering, robotics, and engineering (e.g. power systems and control engineering). Recently the three regions of rough sets are interpreted as regions ...
Dirty data, also known as rogue data, [1] are inaccurate, incomplete or inconsistent data, especially in a computer system or database. [2]Dirty data can contain such mistakes as spelling or punctuation errors, incorrect data associated with a field, incomplete or outdated data, or even data that has been duplicated in the database.
ELKI is an open-source Java data mining toolkit that contains several anomaly detection algorithms, as well as index acceleration for them. PyOD is an open-source Python library developed specifically for anomaly detection. [56] scikit-learn is an open-source Python library that contains some algorithms for unsupervised anomaly detection.
The actual data mining task is the semi-automatic or automatic analysis of massive quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining).
The importance of point-in-time consistency can be illustrated with what would happen if a backup were made without it. Assume Wikipedia's database is a huge file, which has an important index located 20% of the way through, and saves article data at the 75% mark. Consider a scenario where an editor comes and creates a new article at the same time a backup is being performed, which is being ...
Data quality assurance is the process of data profiling to discover inconsistencies and other anomalies in the data, as well as performing data cleansing [17] [18] activities (e.g. removing outliers, missing data interpolation) to improve the data quality.
It is used in combination with its data stream mining plugin (formerly concept drift plugin). EDDM (Early Drift Detection Method): free open-source implementation of drift detection methods in Weka. MOA (Massive Online Analysis): free open-source software specific for mining data streams with concept drift. It contains a prequential evaluation ...