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Data Stream Mining (also known as stream learning) is the process of extracting knowledge structures from continuous, rapid data records. A data stream is an ordered sequence of instances that in many applications of data stream mining can be read only once or a small number of times using limited computing and storage capabilities.
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 ...
Inductive miner frequency-based: The less frequent relations in the event log sometimes creates problems in detecting any type of cuts. In that case, the directly follows relations below a certain threshold are removed from the directly follows graph and the resultant graph is used for detecting the cuts.
SageMath is an open-source math software, [12] with a unified Python interface which is available as a text interface or a graphical web-based one. Includes interfaces for open-source and proprietary general purpose CAS, and other numerical analysis programs, like PARI/GP, GAP, gnuplot, Magma, and Maple.
Mechanisms for discretizing continuous data include Fayyad & Irani's MDL method, [2] which uses mutual information to recursively define the best bins, CAIM, CACC, Ameva, and many others [3] Many machine learning algorithms are known to produce better models by discretizing continuous attributes.
Alternatively, the engine can be called from other programs or used as an API. Individual functions can be called from the command line. RapidMiner provides a variety of learning schemes, models, and algorithms that can be extended using R and Python scripts. [5] RapidMiner can also use plugins available through the RapidMiner Marketplace.
Before data mining algorithms can be used, a target data set must be assembled. As data mining can only uncover patterns actually present in the data, the target data set must be large enough to contain these patterns while remaining concise enough to be mined within an acceptable time limit. A common source for data is a data mart or data ...
Continuous modelling is the mathematical practice of applying a model to continuous data (data which has a potentially infinite number, and divisibility, of attributes). They often use differential equations [ 1 ] and are converse to discrete modelling .