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Database Articles
Page 182 of 547
What is Data Cube Aggregations?
Data integration is the procedure of merging data from several disparate sources. While performing data integration, it must work on data redundancy, inconsistency, duplicity, etc. In data mining, data integration is a record preprocessing method that includes merging data from a couple of the heterogeneous data sources into coherent data to retain and provide a unified perspective of the data.Data integration is especially important in the healthcare industry. Integrated data from several patient records and clinics assist clinicians in identifying medical disorders and diseases by integrating information from several systems into a single perspective of beneficial information from which useful ...
Read MoreWhat are the techniques of Discretization and Concept Hierarchy Generation for Numerical Data?
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 MoreWhat is Data Discretization?
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 MoreDifference between Dimensionality Reduction and Numerosity Reduction?
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 MoreWhat is Numerosity Reduction?
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 MoreWhat is the basic method of attribute subset selection?
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 MoreWhat is Data Reduction?
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 MoreWhat is Data Transformation?
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 MoreWhat is Data Cleaning?
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, 30-40, 40-50, and the imported data includes birth date. The data can be cleans by splitting the data into appropriate types.Types of data cleaningThere are various types of data cleaning which are as follows −Missing Values − Missing values are filled with appropriate values. There are the following approaches to ...
Read MoreWhat is the motivation behind data mining?
Data mining is the procedure of finding useful new correlations, patterns, and trends by sharing through a high amount of data saved in repositories, using pattern recognition technologies including statistical and mathematical techniques. It is the analysis of factual datasets to discover unsuspected relationships and to summarize the records in novel methods that are both logical and helpful to the data owner.It is the procedure of selection, exploration, and modeling of high quantities of information to find regularities or relations that are at first unknown to obtain clear and beneficial results for the owner of the database.It is not limited ...
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