Data Warehousing is an approach that can collect and handle data from multiple sources to provide the business a significant business insight. A data warehouse is specifically created for the goals of support management decisions.In simple terms, a data warehouse defines a database that is maintained independently from an organization’s ... Read More

Measures can be organized into three elements including distributive, algebraic, and holistic. It depends on the type of aggregate functions used.Distributive − An aggregate function is distributive if it can be calculated in a delivered manner as follows. Consider the data are independent into n sets. It can use the ... Read More

Entropy-based discretization is a supervised, top-down splitting approach. It explores class distribution data in its computation and preservation of split-points (data values for separation an attribute range). It can discretize a statistical attribute, A, the method choose the value of A that has the minimum entropy as a split-point, and ... Read More

The utility lies in the fact that the wavelet transformed data can be limited. A compressed approximation of the information can be retained by saving only a small fraction of the principal of the wavelet coefficients. For instance, all wavelet coefficients higher than some user-defined threshold can be maintained. Some ... Read More

Attribute subset selection reduces the data set size by removing irrelevant or redundant attributes (or dimensions). The objective of attribute subset selection is to discover a minimum set of attributes such that the subsequent probability distribution of the data classes is as close as feasible to the original distribution obtained ... Read More

Trend analysis defines the techniques for extracting a model of behavior in a time series that can be slightly or entirely hidden by noise. The methods of trend analysis have been generally used in detecting outbreaks and unexpected increases or decreases in disease appearances, monitoring the trends of diseases, evaluating ... Read More

Temporal data mining defines the process of extraction of non-trivial, implicit, and potentially essential data from large sets of temporal data. Temporal data are a series of primary data types, generally numerical values, and it deals with gathering beneficial knowledge from temporal data.The objective of temporal data mining is to ... Read More

Cluster analysis is a branch of statistics that has been studied widely for several years. The benefit of using this technique is that interesting structures or clusters can be discovered directly from the data without utilizing any background knowledge, such as concept hierarchy.Clustering algorithms used in statistics, like PAM or ... Read More

Spatial data mining is the application of data mining to spatial models. In spatial data mining, analysts use geographical or spatial data to make business intelligence or different results. This needed specific methods and resources to get the geographical data into relevant and beneficial formats.There are several challenges involved in ... Read More

Web mining defines the process of using data mining techniques to extract beneficial patterns trends and data generally with the help of the web by dealing with it from web-based records and services, server logs, and hyperlinks. Web mining aims to discover the designs in web information by grouping and ... Read More