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Recent advances in 3D acquisition techniques have led to a rapid increase in the size of database of three dimensional (3D) models across areas as diverse as engineering, medicine and biology, etc.
Several algorithms have been presented to mine high-utility itemsets (HUIs) and most of the designed algorithms have to handle the exponential search space for discovering HUIs when the number of distinct items and the size of database are very large.
However, by increasing the size of database, the amount of this improvement is slightly reduced.
However, the size of database low processing speed which not explored further.
Constraints on the size of database we can capture means that it is impossible to store exemplars for all phonemes in all contexts.
We not only discuss the statistics of these databases but also present a comparative analysis on different dimensions including the size of database, number of contributors, textual content of the database, data acquisition mode (online or offline), writing script and the tasks which could be evaluated on a given database.
Similar(47)
That achievement results from exponential gains in computing speed and the size of databases, as well as big advances in software.
We study an illustrative example on the performance benefits in relationship to the size of databases with regard to the proposed architectural model.
We challenge that premise suggesting a selective retention approach for operational data thus curtailing the size of databases and warehouses without losing content and information value.
Prototype selection problem consists of reducing the size of databases by removing samples that are considered noisy or not influential on nearest neighbour classification tasks.
The associated computational cost has limited the size of databases.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com