This data article provides detailed optimization input and output datasets and optimization code for the published research work titled "Dynamic green supplier selection and order allocation with quantity discounts and varying supplier availability" (Hamdan and Cheaitou, 2017, In press) [1]. Researchers may use these datasets as a baseline for future comparison and extensive analysis of the gre...

Association rule mining aims to explore large transaction databases for association rules. Classical Association Rule Mining (ARM) model assumes that all items have the same significance without taking their weight into account. It also ignores the difference between the transactions and importance of each and every itemsets. But, the Weighted Association Rule Mining (WARM) does not work on dat...

Knowledge inference from semi-structured data can utilize frequent sub structures, in addition to frequency of data items. In fact, the working assumption of the present study is that frequent sub-trees of XML data represent sets of tags (objects) that are meaningfully associated. A method for extracting frequent sub-trees from XML data is presented. It uses thresholds on frequencies of paths a...

A Parallel Frequent Item sets mining algorithm called FiDoop using MapReduce programming model. FiDoop includes the frequent items ultrametric tree(FIU-tree), in that three MapReduce jobs are applied to complete the mining task. The scalability problem has been addressed bythe implementation of a handful of FP-growth-like parallelFIM algorithms. InFiDoop, the mappers independently and concurren...

Frequent pattern mining is the process of mining data in a set of items or some patterns from a large database. The resulted frequent set data supports the minimum support threshold. A frequent pattern is a pattern that occurs frequently in a dataset. Association rule mining is defined as to find out association rules that satisfy the predefined minimum support and confidence from a given data ...

introduction: health information plays a vital role in managers' decision making and planning. health information in the form of medical records is also useful for caregivers, quality assessment, financial payments, legal measures, education, research and planning. in this study, to examine the managers and personnel's viewpoints about the application of medical records. methods and material: i...

Classical association rule mining algorithm discovers frequent itemsets from transactional databases by considering the appearance of the itemset and not other utilities such as profit of an item or quantity in which items bought. But in transactional databases large quantity of items is purchased may lead to very high profit even though items appeared in few transactions. Therefore the quantit...

Item-level analysis allows for the examination of qualitative age and individual differences in skill acquisition, which are obscured when aggregating data across items. In the present study, item-level strategy shifts were generally gradual and variable, rather than abrupt and collective. Strategy shift reversions were frequent, and the total transition space was extensive, for both younger an...

—Frequent itemset mining has emerged as a fundamental problem in data mining and plays an important role in many data mining tasks, such as association analysis, classification, etc. In the framework of frequent itemset mining, the results are itemsets that are frequent in the whole database. However, in some applications, such recommendation systems and social networks, people are more interes...

Prominent neurobiological models of language follow the widely accepted assumption that language comprehension requires two principal mechanisms: a lexicon storing the sound-to-meaning mapping of words, primarily involving bilateral temporal regions, and a combinatorial processor for syntactically structured items, such as phrases and sentences, localized in a left-lateralized network linking l...