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Attribute generalization depends on the following rule: If there is a huge collection of distinct values for an attribute in the original working relation, and there exists a group of generalization operators on the attribute, thus a generalization operator should be choose and utilized to the attribute.This rule depends on the following reasoning. The use of a generalization services to generalize an attribute value inside a tuple, or rule, in the working relation will create the rule cover more of the initial data tuples, therefore generalizing the concept it defines. This corresponds to the generalization rule defined as climbing generalization ... Read More
AOI stands for Attribute-Oriented Induction. The attribute-oriented induction approach to concept description was first proposed in 1989, a few years before the introduction of the data cube approach. The data cube approach is essentially based on materialized views of the data, which typically have been pre-computed in a data warehouse.In general, it implements off-line aggregation earlier an OLAP or data mining query is submitted for processing. In other words, the attribute-oriented induction approach is generally a query-oriented, generalization-based, on-line data analysis methods.The general idea of attribute-oriented induction is to first collect the task-relevant data using a database query and then ... Read More
Data generalization summarizes data by replacing relatively low-level values (such as numeric values for an attribute age) with higher-level concepts (such as young, middleaged, and senior). Given the high amount of data saved in databases, it is beneficial to be able to define concepts in concise and succinct terms at generalized (rather than low) methods of abstraction.It is allowing data sets to be generalized at multiple levels of abstraction facilitates users in examining the general behavior of the data. Given the AllElectronics database, for instance, rather than examining single customer transactions, sales managers can prefer to view the data generalized ... Read More
The curse of dimensionality and the need for understandable results pose serious challenges for finding an efficient and scalable solution to the cubegrade problem. It can be confined but interesting version of the cubegrade problem, called constrained multidimensional gradient analysis. It can reduces the search space and derives interesting results.There are the following types of constraints which are as follows −Significance constraint − This provide that it can test only the cells that have specific “statistical significance” in the data, including containing at least a defined number of base cells or at least a specific total sales. In the data ... Read More
To return the lower triangle of an array, use the numpy.tril() method in Python Numpy The 1st parameter is the input array. The 2nd parameter is the 'k' i.e. the diagonal above which to zero elements. Here, k = 0 (the default) is the main diagonal, k < 0 is below it and k > 0 is above.The k = -1 value is to zero the main diagonal as wellThe function returns a copy of an array with elements above the k-th diagonal zeroed. For arrays with ndim exceeding 2, tril will apply to the final two axes.StepsAt first, import ... Read More
There are three measures are used as exception indicators to support recognize data anomalies. These measures denotes the degree of surprise that the quantity in a cell influence, concerning its expected value.The measures are computed and associated with every cell, for all levels of aggregation. They are as follows including the SelfExp, InExp, and PathExp measures are based on a numerical approaches for table analysis.A cell value is treated an exception depends on how much it differs from its expected value, where its expected value is decided with a statistical model. The difference among a given cell value and its ... Read More
To return the truncated value of the input, use the numpy.trunc() method in Python Numpy. The function returns the truncated value of each element in x. This is a scalar if x is a scalar. The truncated value of the scalar x is the nearest integer i which is closer to zero than x is. In short, the fractional part of the signed number x is discarded.The condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note ... Read More
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
To return the truncated value of the array elements, use the numpy.trunc() method in Python Numpy. The function returns the truncated value of each element in x. This is a scalar if x is a scalar. The truncated value of the scalar x is the nearest integer i which is closer to zero than x is. In short, the fractional part of the signed number x is discarded.The out is a location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. ... Read More
To return the floor of a specific array element, use the index value in the numpy.floor() method in Python Numpy. The floor of the scalar x is the largest integer i, such that i