Organizing research data

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Organizing research data

Research relies on ever larger amounts of data from experiments, automated production equipment, questionnaries, times series such as weather records, and so on. A major task in science is to combine, process and analyse such data to obtain evidence of patterns and correlations.Most research data are on digital form, which in principle ensures easy processing and analysis, easy long-term preser...

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Self-Organizing Data Structures

We survey results on self-organizing data structures for the search problem and concentrate on two very popular structures: the un-sorted linear list, and the binary search tree. For the problem of maintaining unsorted lists, also known as the list update problem, we present results on the competitiveness achieved by deterministic and random-ized on-line algorithms. For binary search trees, we ...

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A Modfied Self-organizing Map Neural Network to Recognize Multi-font Printed Persian Numerals (RESEARCH NOTE)

This paper proposes a new method to distinguish the printed digits, regardless of font and size, using neural networks.Unlike our proposed method, existing neural network based techniques are only able to recognize the trained fonts. These methods need a large database containing digits in various fonts. New fonts are often introduced to the public, which may not be truly recognized by the Opti...

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Extended Self - Organizing Map on Transactional Data

In many application domains, transactions are the records of personal activities. Transactions always reveal personal behavior customs, so clustering the transactional data can divide individuals into different segments. Transactional data are often accompanied with a concept hierarchy, which defines the relevancy among all of the possible items in transactional data. However, most of clusterin...

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Mining Biological Data Using Self-Organizing Map

This paper presents a novel method of mining biological data using a self-organizing map (SOM). After partitioning a set of protein sequences using SOM, conventional homology alignment is applied to each cluster to determine the conserved local motif (biological pattern) for the cluster. These local motifs are then regarded as rules for prediction and classification. In the application to the p...

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ژورنال

عنوان ژورنال: Acta Veterinaria Scandinavica

سال: 2011

ISSN: 1751-0147

DOI: 10.1186/1751-0147-53-s1-s2