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

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 More

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 ... Read More

The data mining system is integrated with a database or data warehouse system so that it can do its tasks in an effective presence. A data mining system operates in an environment that needed it to communicate with other data systems like a database system. There are the possible integration schemes that can integrate these systems which are as follows −No coupling − No coupling defines that a data mining system will not use any function of a database or data warehouse system. It can retrieve data from a specific source (including a file system), process data using some data ... Read More

Data mining functionalities are used to represent the type of patterns that have to be discovered in data mining tasks. In general, data mining tasks can be classified into two types including descriptive and predictive. Descriptive mining tasks define the common features of the data in the database and the predictive mining tasks act inference on the current information to develop predictions.There are various data mining functionalities which are as follows −Data characterization − It is a summarization of the general characteristics of an object class of data. The data corresponding to the user-specified class is generally collected by a ... Read More