What is Noise in Data Mining? Noisy data are data with a large amount of additional meaningless information called noise.
- 1. What is Noise in Data Mining? • Noisy data are data with a large amount of additional meaningless information called noise. This includes data corruption, and the term is often used as a synonym for corrupt data. It also includes any data that a user system cannot understand and interpret correctly. • Improper procedures (or improperly-documented procedures) to subtract out the noise in data can lead to a false sense of accuracy or false conclusions. • Data = true signal + noise • Noisy data unnecessarily increases the amount of storage space required and can adversely affect any data mining analysis results. x
- 2. How to Manage Noisy Data? Removing noise from a data set is termed data smoothing. The following ways can be used for Smoothing: 1. Binning There are three methods for smoothing data in the bin.
- 11. • A density-based methods typically measure distances between individual data points and the rest of their respective groups. This is the approach taken by density-based methods, where anomalies are defined as observations of low probability. • A distance-based outlier detection method consults the neighborhood of an object, which is defined by a given radius. An object is then considered an outlier if its neighborhood does not have enough other points. • The grid-based method in outlier detection involves dividing the data space into a grid or many cells. Here Each cell contains a group of data points. The density and the number of data points in each cell are noted. The cells with low data point density are identified as outliers. • Deviation- based can reveal surprising facts hidden inside data. CMSR Data Miner (Cramer Modeling, Segmentation and Rules )provides tools that can be used to detect deviations, anomalies, and outliers.