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pro vyhledávání: '"Turenne, Nicolas"'
Autor:
Turenne, Nicolas
Large amounts of data are available due to low-cost and high-capacity data storage equipments. We propose a data exploration/visualization method for tabular multi-dimensional, time-varying datasets to present selected items in their global context.
Externí odkaz:
http://arxiv.org/abs/1505.05136
Autor:
Turenne, Nicolas
In several domains such as linguistics, molecular biology or social sciences, holistic effects are hardly well-defined by modeling with single units, but more and more studies tend to understand macro structures with the help of meaningful and useful
Externí odkaz:
http://arxiv.org/abs/1606.00414
Autor:
Turenne, Nicolas
We present a new method to detect duplicates used to merge different bibliographic record corpora with the help of lexical and social information. As we show, a trivial key is not available to delete useless documents. Merging heteregeneous document
Externí odkaz:
http://arxiv.org/abs/1504.07597
Autor:
Turenne, Nicolas, Phan, Tien
Relation extraction with accurate precision is still a challenge when processing full text databases. We propose an approach based on cooccurrence analysis in each document for which we used document organization to improve accuracy of relation extra
Externí odkaz:
http://arxiv.org/abs/1504.06078
Important data are locked in ancient literature. It would be uneconomic to produce these data again and today or to extract them without the help of text mining technologies. Vespa is a text mining project whose aim is to extract data on pest and cro
Externí odkaz:
http://arxiv.org/abs/1504.06077
Autor:
Turenne, Nicolas
We present a new R package which takes a numerical matrix format as data input, and computes clusters using a support vector clustering method (SVC). We have implemented an original 2D-grid labeling approach to speed up cluster extraction. In this se
Externí odkaz:
http://arxiv.org/abs/1504.06080
Autor:
Turenne, Nicolas
Publikováno v:
Journal of Data Mining & Digital Humanities, 2014 (June 24, 2014) jdmdh:4
Text data is often seen as "take-away" materials with little noise and easy to process information. Main questions are how to get data and transform them into a good document format. But data can be sensitive to noise oftenly called ambiguities. Ambi
Externí odkaz:
http://arxiv.org/abs/1311.5401