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Cheetah (or CheetahTemplate) is a template engine that uses the Python programming language.It can be used standalone or combined with other tools and frameworks. It is often used for server-side scripting and dynamic web content by generating HTML, but can also be used to generate source code.
This template is used to display Coxeter–Dynkin diagrams (CDDs). To use the template, list the parts of the diagram from left to right, separated by vertical-bar (pipe) characters (" | "). For example:
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.
KNIME (/ n aɪ m / ⓘ), the Konstanz Information Miner, [2] is a free and open-source data analytics, reporting and integration platform.KNIME integrates various components for machine learning and data mining through its modular data pipelining "Building Blocks of Analytics" concept.
Dia loads and saves diagrams in a custom XML format which is, by default, gzipped to save space. It can print large diagrams spanning multiple pages [ 4 ] and can also be scripted using the Python programming language .
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.
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 .
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.