It is complex and laborious to define concept hierarchies for numerical attributes because of the broad diversity of applicable data ranges and the frequent updates of data values. There are various methods of concept hierarchy generation for numeric data are as follows −Binning − Binning is a top-down splitting technique ... Read More

The data discretization techniques can be used to reduce the number of values for a given continuous attribute by dividing the range of the attribute into intervals. Interval labels can be used to restore actual data values. It can be restoring multiple values of a continuous attribute with a small ... Read More

Dimensionality ReductionIn dimensionality reduction, data encoding or transformations are used to access a reduced or “compressed” depiction of the original data. If the original data can be regenerated from the compressed data without any loss of data, the data reduction is known as lossless. If data reconstructed is only approximated ... Read More

In the Numerosity reduction, the data volume is reduced by choosing an alternative, smaller form of data representation. These techniques may be parametric or nonparametric. For parametric methods, a model is used to estimate the data, so that only the data parameters need to be stored, instead of the actual ... Read More

In dimensionality reduction, data encoding or transformations are applied to obtain a reduced or “compressed” representation of the original data. If the original data can be reconstructed from the compressed data without any failure of information, the data reduction is known as lossless. If data reconstructed is only approximated of ... Read More

Attribute subset selection decreases the data set size by eliminating irrelevant or redundant attributes (or dimensions). Attribute subset selection aims to discover a minimum set of attributes such that the resulting probability distribution of the data classes is as close as applicable to the original distribution accessing using all attributes. ... Read More

Data mining is applied to the selected data in a large amount database. When data analysis and mining is done on a huge amount of data then it takes a very long time to process, which makes it impractical and infeasible. It can reduce the processing time for data analysis, ... Read More

In data transformation, the data are transformed or combined into forms suitable for mining. Data transformation can involve the following −Smoothing − It can work to remove noise from the data. Such methods contain binning, regression, and clustering.Aggregation − In aggregation, where summary or aggregation operations are applied to the ... Read More

Data integration is the phase of combining data from several disparate sources. While implementing data integration, it should work on data redundancy, inconsistency, duplicity, etc. In data mining, data integration is a data pre-processing technique that contains merging data from numerous heterogeneous data sources into coherent data to retain and ... Read More

Data cleaning defines to clean the data by filling in the missing values, smoothing noisy data, analyzing and removing outliers, and removing inconsistencies in the data. Sometimes data at multiple levels of detail can be different from what is required, for example, it can need the age ranges of 20-30, ... Read More