This service allows you to extraxt bibliographic metadata from numerous digital libraries. The extracted data is represented in BibTeX format.
You can also drag the
button to the links toolbar of your browser once and then use it to scrape publications from pages listed here by pressing
the button on one of the listed pages.
The service accepts the following parameters:
- url: The URL of the page which should be scraped.
- selection: Text selected on the web page. Used by some scrapers in addition to / instead of the URL.
- format: If equal to bibtex, plain BibTeX is returned instead of this HTML page. Experimental:
rdf+xml
scraped URL
http://www.sciencedirect.com/science/article/pii/S0031320310000051
active scraper
org.bibsonomy.scraper.url.kde.science.ScienceDirectScraper: This scraper parses a publication page from ScienceDirect.
resulting BibTeX
post to BibSonomyget plain BibTeX
@article{KASHEF20102315, title = {Cooperative clustering}, journal = {Pattern Recognition}, volume = {43}, number = {6}, pages = {2315-2329}, year = {2010}, issn = {0031-3203}, doi = {https://doi.org/10.1016/j.patcog.2009.12.018}, url = {https://www.sciencedirect.com/science/article/pii/S0031320310000051}, author = {Rasha Kashef and Mohamed S. Kamel}, keywords = {Cooperative clustering, Similarity histogram, Cooperative contingency graph}, abstract = {Data clustering plays an important role in many disciplines, including data mining, machine learning, bioinformatics, pattern recognition, and other fields, where there is a need to learn the inherent grouping structure of data in an unsupervised manner. There are many clustering approaches proposed in the literature with different quality/complexity tradeoffs. Each clustering algorithm works on its domain space with no optimum solution for all datasets of different properties, sizes, structures, and distributions. In this paper, a novel cooperative clustering (CC) model is presented. It involves cooperation among multiple clustering techniques for the goal of increasing the homogeneity of objects within the clusters. The CC model is capable of handling datasets with different properties by developing two data structures, a histogram representation of the pair-wise similarities and a cooperative contingency graph. The two data structures are designed to find the matching sub-clusters between different clusterings and to obtain the final set of clusters through a coherent merging process. The cooperative model is consistent and scalable in terms of the number of adopted clustering approaches. Experimental results show that the cooperative clustering model outperforms the individual clustering algorithms over a number of gene expression and text documents datasets.} }