نتایج جستجو برای: high average utility itemset
تعداد نتایج: 2450146 فیلتر نتایج به سال:
Set-valued data provides enormous opportunities for various data mining tasks. In this paper, we study the problem of publishing set-valued data for data mining tasks under the rigorous differential privacy model. All existing data publishing methods for set-valued data are based on partitionbased privacy models, for example k-anonymity, which are vulnerable to privacy attacks based on backgrou...
The paradigm shift from ‘data-centered pattern mining’ to ‘domain driven actionable knowledge discovery’ has increased the need for considering the business yield (utility) and demand or rate of recurrence of the items (frequency) while mining a retail business transaction database. Such a data mining process will help in mining different types of itemsets of varying business utility and demand...
This work proposes an efficient mining algorithm to find maximal frequent item sets from relational database. It adapts to large datasets.Itemset is stored in list with special structure. The two main lists called itemset list and Frequent itemset list are created by scanning database once for dividing maximal itemsets into two categories depending on whether the itemsets to achieve minimum sup...
This work proposes an efficient mining algorithm to find maximal frequent item sets from relational database. It adapts to large datasets.Itemset is stored in list with special structure. The two main lists called itemset list and Frequent itemset list are created by scanning database once for dividing maximal itemsets into two categories depending on whether the itemsets to achieve minimum sup...
Over the last decades, frequent itemset mining has become a major area of research, with applications including indexing and similarity search, as well as mining of data streams, web, and software bugs. Although several efficient techniques for generating frequent itemsets with a minimum support (frequency) have been proposed, the number of itemsets produced is in many cases too large for effec...
In recent years, due to the wide applications of uncertain data, mining frequent itemsets over uncertain databases has attracted much attention. In uncertain databases, the support of an itemset is a random variable instead of a fixed occurrence counting of this itemset. Thus, unlike the corresponding problem in deterministic databases where the frequent itemset has a unique definition, the fre...
High-utility itemset mining (HUIM) is a critical issue in recent years since it can be used to reveal the profitable products by considering both the quantity and profit factors instead of frequent itemset mining (FIM) or association-rule mining (ARM). Several algorithms have been presented tomine high-utility itemsets (HUIs) andmost of them have to handle the exponential search space for disco...
according to research, academic self-concept and academic achievement are mutually interdependent. in the present study, the aim was to determine the relationship between the academic self-concept and the academic achievement of students in english as a foreign language and general subjects. the participants were 320 students studying in 4th grade of high school in three cities of noor, nowshah...
the purpose of the present study was to investigate the relationship between fear of negative evaluation (fne) and communication strategies (css) among iranian efl learners. it was aimed to examine the differences in the use of communication strategies between speakers with high or low degree of fear of negative evaluation. the current study was a case study consisting of 10 english learners at...
The purpose of association mining is to find the valuable relationships between data sets. The prerequisite of it is to find the frequent itemset first. In view of the existing problems in the present frequent itemset mining, this paper puts forward that data sets should be clustered first, and then the algorithm of frequent itemset mining be applied to every cluster. In this way, algorithm of ...
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