[3] If there is a high proportion of irrelevant and redundant information present or noisy and unreliable data, then knowledge discovery during the training phase may be more difficult.
Editing such dataset to either correct data corruption or human error is a crucial step to get accurate quantifiers like true positives, true negatives, false positives and false negatives found in a confusion matrix that are commonly used for a medical diagnosis.
Users use Python programming scripts accompanied by the pandas library which gives them the ability to import data from a comma-separated values as a data-frame.
[4] More advanced techniques like principal component analysis and feature selection are working with statistical formulas and are applied to complex datasets which are recorded by GPS trackers and motion capture devices.
It does this by using working as set of prior knowledge to reduce the space required for searching and acting as a guide to the data.
[citation needed] In general, the use of ontologies bridges the gaps between data, applications, algorithms, and results that occur from semantic mismatches.
[citation needed] Applications include the medical field, language processing, banking,[8] and even tutoring,[9] among many more.
Additionally, well-structured formal semantics integrated into well designed ontologies can return powerful data that can be easily read and processed by machines.
[11] This would allow the first responders to quickly and efficiently search for medicine without having worry about the patient’s medical history themselves, as the semantic reasoner would already have analyzed this data and found solutions.
[12] This could result in higher costs and increased difficulties in building and maintaining semantic data processing systems.
[tone] Below is a simple a diagram combining some of the processes, in particular semantic data mining and their use in ontology.
Ultimately, fuzzy data mining's goal is to help deal with inexact information, such as an incomplete database.