Found 427 Articles for Data Mining

Discovery-driven exploration is such a cube exploration approach. In discovery-driven exploration, precomputed measures indicating data exceptions are used to guide the user in the data analysis process, at all levels of aggregation. It refer to these measures as exception indicators.Intuitively, an exception is a data cube cell value that is significantly different from the value anticipated, based on a statistical model. The model treated variations and patterns in the measure value across all of the dimensions to which a cell apply.For instance, if the analysis of item-sales data acknowledge an increase in sales in December in comparison to several months, ... Read More

Data Warehousing is an approach that can collect and manage information from multiple sources to support the business a significant business insight. A data warehouse is specifically created for the goals of support management decisions.A data warehouse defines a database that is maintained separately from a company operational databases. Data warehouse systems enable the integration of several application systems. They support data processing by supporting a solid platform of consolidated, historical records for analysis.A data warehouse can be considered as a group of materialized views defined over remote base areas. When a query is formal, it is computed locally, using ... Read More

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 operational databases. Data warehouse systems enable the integration of several application systems. They provide data processing by supporting a solid platform of consolidated, historical data for analysis.A data warehouse supports an OLTP system by providing a place for the OLTP database to offload records as it accumulates, and by providing ... 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 service to each partition, resulting in n aggregate values.If the result changed by using the function to the n aggregate values is the same as that derived by using the function to the whole data set (without partitioning), the function can be evaluated in a distributed way.For instance, count() can ... 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 recursively divisions the resulting intervals to appear at a hierarchical discretization.Specific discretization forms a concept hierarchy for A. Let D includes data tuples described by a group of attributes and a class-label attribute. The class-label attribute supports the class data per tuple. The basic approach for the entropy-based discretization of ... 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 other coefficients are set to 0.The resulting data description is very sparse so that services that can take benefit of data sparsity are computationally very quick if implemented in wavelet space. The method also works to eliminate noise without smoothing out the main characteristics of the data, creating it efficient ... 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 using all attributes.For n attributes, there are 2n possible subsets. An exhaustive search for the optimal subset of attributes can be extremely costly, specifically as n and the number of data classes raise. Hence, heuristic approaches that explore a reduced search space are generally used for attribute subset selection.These approaches ... 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 the effectiveness of disease control programs and policies, and assessing the success of health care programs and policies, etc.Various techniques can be used to detect trends in item series. Smoothing is an approach that is used to remove the non-systematic behaviors found in time series. Smoothing usually takes the form ... 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 find temporal patterns, unexpected trends, or several hidden relations in the higher sequential data, which is composed of a sequence of nominal symbols from the alphabet referred to as a temporal sequence and a sequence of continuous real-valued components called a time series, by utilizing a set of approaches from ... 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 CLARA, are reported to be inefficient from the computational complexity point of view. As per the efficiency concern, a new algorithm called CLARANS (Clustering Large Applications based upon Randomized Search) was developed for cluster analysis.PAM (Partitioning around Medoids) − It is assuming that there are n objects, PAM finds k ... Read More