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Data Structure Articles
Page 119 of 164
How are metarules useful in data mining?
Data mining is the process of finding useful new correlations, patterns, and trends by transferring 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.Data Mining is similar ...
Read MoreWhat is Constraint-Based Association Mining?
A data mining procedure can uncover thousands of rules from a given set of information, most of which end up being independent or tedious to the users. Users have a best sense of which “direction” of mining can lead to interesting patterns and the “form” of the patterns or rules they can like to discover.Therefore, a good heuristic is to have the users defines such intuition or expectations as constraints to constraint the search space. This strategy is called constraint-based mining.Constraint-based algorithms need constraints to decrease the search area in the frequent itemset generation step (the association rule generating step ...
Read MoreWhat are the steps involved in Association Rule Clustering System?
There are the following steps are involved in association rule clustering system which are as follows −Binning − Quantitative attributes can have a broad range of values representing their domain. It can think about how big a 2-D grid would be if it can plotted age and income as axes, where every possible value of age was created a specific position on one axis, and same, every possible value of income was created a specific position on the other axis.It can maintain grids down to a manageable size, it can instead partition the areas of quantitative attributes into intervals. These ...
Read MoreHow can we mine closed frequent itemsets?
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 start to remove redundant itemsets. A recommended techniques is to search for closed frequent itemsets precisely during the mining phase. This needed us to prune the search area as soon as it can identify the method of closed itemsets during mining. There are various pruning strategies include the following −Item ...
Read MoreWhat are the criteria of frequent pattern mining?
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 itemsets (It can satisfy a collection of user-defined constraints), approximate frequent itemsets (It can change only approximate support counts for the mined frequent itemsets), near-match frequent itemsets (It can count the support count of the relatively matching itemsets), top-k frequent itemsets (i.e., the k most frequent itemsets for a user-specified ...
Read MoreHow is class comparison performed?
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 are comparable classes, and so are computer science candidates versus physics candidates.The techniques developed can be continued to manage class comparison across multiple comparable classes. For instance, the attribute generalization process defined for class characterization can be changed so that the generalization is implemented synchronously between all the classes compared. ...
Read MoreWhat are the rules of Attribute Generalization?
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 MoreWhat is AOI?
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 MoreWhat are the methods for Data Generalization and Concept Description?
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 MoreWhat is the types of constraints in multidimensional gradient analysis?
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 ...
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