Data reduction
Data reduction is the transformation of numerical or alphabetical
When information is derived from instrument readings there may also be a transformation from
An example in
Research has also been carried out on the use of data reduction in wearable (wireless) devices for health monitoring and diagnosis applications. For example, in the context of epilepsy diagnosis, data reduction has been used to increase the battery lifetime of a wearable EEG device by selecting and only transmitting EEG data that is relevant for diagnosis and discarding background activity.[2]
Types of Data Reduction
Dimensionality Reduction
When dimensionality increases, data becomes increasingly sparse while density and distance between points, critical to clustering and outlier analysis, becomes less meaningful. Dimensionality reduction helps reduce noise in the data and allows for easier visualization, such as the example below where 3-dimensional data is transformed into 2 dimensions to show hidden parts. One method of dimensionality reduction is wavelet transform, in which data is transformed to preserve relative distance between objects at different levels of resolution, and is often used for image compression.[3]
Numerosity Reduction
This method of data reduction reduces the data volume by choosing alternate, smaller forms of data representation. Numerosity reduction can be split into 2 groups: parametric and non-parametric methods. Parametric methods (regression, for example) assume the data fits some model, estimate model parameters, store only the parameters, and discard the data. One example of this is in the image below, where the volume of data to be processed is reduced based on more specific criteria. Another example would be a log-linear model, obtaining a value at a point in m-D space as the product on appropriate marginal subspaces. Non-parametric methods do not assume models, some examples being histograms, clustering, sampling, etc.[4]
Statistical modelling
Data reduction can be obtained by assuming a statistical model for the data. Classical principles of data reduction include sufficiency, likelihood, conditionality and equivariance.[5]
See also
References
- ^ "Travel Time Data Collection Handbook" (PDF). Retrieved 6 December 2020.
- S2CID 24852887.
- ^ Han, J.; Kamber, M.; Pei, J. (2011). "Data Mining: Concepts and Techniques (3rd ed.)" (PDF). Retrieved 6 December 2020.
- ^ Han, J.; Kamber, M.; Pei, J. (2011). "Data Mining: Concepts and Techniques (3rd ed.)" (PDF). Retrieved 6 December 2020.
- OCLC 46538638.
Further reading
- ISBN 0-471-10134-6.