In naïve approach, it can mine the complete set of frequent itemsets and then remove each frequent itemset that is a proper subset of, and give the similar support as, a current frequent itemset.This method can derive 2100−1 frequent itemsets to obtain a length-100 frequent itemset, all before it can ... Read More

Apriori is a seminal algorithm developed by R. Agrawal and R. Srikant in 1994 formining frequent itemsets for Boolean association rules. The algorithm depends on the case that the algorithm need previous knowledge of frequent itemset properties.Apriori use an iterative method called a level-wise search, where k-itemsets can explore (k+1)-itemsets. ... Read More

There are several criteria of frequent pattern mining which are as follows −Based on the completeness of patterns to be mined − It can mine the whole collection of frequent itemsets, the closed frequent itemsets, and the maximal frequent itemsets, provided a minimum support threshold.It can also extract constrained frequent ... Read More

Class discrimination or comparison mines characterization that categorize a target class from its contrasting classes. The target and contrasting classes should be comparable providing they share same dimensions and attributes. For instance, the three classes, person, address, and elements, are not comparable. But the sales in the last three years ... Read More

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 ... 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 ... 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 ... 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 ... 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 ... 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 ... Read More