Found 427 Articles for Data Mining

Categorical data are discrete data. Categorical attributes have a fixed number of distinct values, with no sequencing among the values involving geographic area, job category, and item type. There are various methods for the generation of concept hierarchies for categorical data are as follows −Specification of a partial ordering of attributes explicitly at the schema level by users or experts − Concept hierarchies for categorical attributes or dimensions generally contain a group of attributes. A user or professional can simply represent a concept hierarchy by defining a partial or total ordering of the attributes at the schema level.For instance, a ... Read More

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 based on a defined number of bins. These methods are also used as discretization methods for numerosity reduction and concept hierarchy generation. These techniques can be used recursively to the resulting partitions to make concept hierarchies. Binning does not use class data and is, therefore, an unsupervised discretization technique. It ... 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 number of interval labels therefore decrease and simplifies the original information.This leads to a concise, easy-to-use, knowledge-level representation of mining results. Discretization techniques can be categorized depends on how the discretization is implemented, such as whether it uses class data or which direction it proceeds (i.e., top-down vs. bottom-up). If ... 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 of the original data, then the data reduction is called lossy.The DWT is nearly associated with the discrete Fourier transform (DFT), a signal processing technique containing sines and cosines. In general, the DWT achieves better lossy compression. That is if a similar number of coefficients is maintained for a DWT ... 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 data, for example, Log-linear models. Non-parametric methods are used for storing a reduced representation of the data which include histograms, clustering, and sampling.There are the following techniques of numerosity reduction which are as follows −Regression and Log-Linear Models − These models can be used to approximate the given data. In ... 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 the original data, then the data reduction is called lossy.There are two methods of lossy reduction which are as follows −Wavelet Transforms − The discrete wavelet transform (DWT) is a linear signal processing technique that, when applied to a data vector X, transforms it to a numerically different vector, X’, ... 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. Data mining on a reduced set of attributes has an extra benefit. It reduces the multiple attributes occurring in the discovered patterns, provides to create the patterns simpler to understand.For n attributes, there are 2n possible subsets. An exhaustive search for the optimal subset of attributes can be intensely expensive, ... 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, data reduction techniques are used to obtain a reduced representation of the dataset that is much smaller in volume by maintaining the integrity of the original data. By reducing the data, the efficiency of the data mining process is improved which produces the same analytical results.Data reduction aims to define ... 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 data. For example, the daily sales data may be aggregated to compute monthly and annual total amounts. This phase is generally used in making a data cube for the analysis of the data at multiple granularities.Generalization − In Generalization, where low-level or “primitive” (raw) data are restored by larger-level concepts ... 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 support a consolidated perspective of the information.It combines data from various sources into a coherent data store, including in data warehousing. These sources can involve multiple databases, data cubes, or flat files, etc. There are multiple issues to consider during data integration.Schema integration and object matching can be complex. For ... Read More