Abstract
Over time, many clustering methods were proposed, but there are many specific areas where adaptations, customizations and modifications of classical clustering algorithms are needed in order to achieve better results. The present article proposes a technique which uses a custom patent model, aiming to improve the quality of clustering by emphasizing the importance of various patent metadata. This can be achieved by computing different weights for different patent metadata attributes, which are considered to be valuable information.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Pressman, D.: Patent It Yourself, 11th edn., Nolo (2005)
Delphion – Text Clustering, http://www.delphion.com/products/research/products-cluster
Manish, S.: Text Clustering on Patents. White Paper. Gridlogics Tech. Pvt. Ltd. (2009)
PatentCluster, http://www.patentcluster.com/
Vlase, M., Munteanu, D.: Patent relevancy on patent databases. In: Networking in Education and Research, Proceedings of the 8th RoEduNet International Conference (2009)
WIPO – Glossary, http://www.wipo.int/pctdb/en/glossary.jsp#p
WIPO Guide to Using PATENT INFORMATION. WIPO Publication No. L434/3(E) (2010) ISBN 978-92-805-2012-5
Giereth, M., Brügmann, S., Stäbler, A., Rotard, M., Ertl, T.: Application of semantic technologies for representing patent metadata. In: Proceedings of the First International Workshop on Applications of Semantic Technologies (2006)
Chau, M., Huang, Z., Qin, J., Zhou, Y., Chen, H.: Building a scientific knowledge web portal: The NanoPort experience. Decision Support Systems 42(2), 1216–1238 (2006)
MacQueen, J.: Some Methods for Classification and Analysis of Multivariate Observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)
Hartigan, J.: Clustering Algorithms. John Wiley & Sons, New York (1975)
Hartigan, J., Wong, M.: Algorithm AS136: A k-means clustering algorithm. Applied Statistics 28, 100–108 (1979)
Modha, D., Spangler, S.W.: Feature weighting in k-means clustering. Machine Learning 52(3) (2003)
Berkhin, P.: A Survey of Clustering Data Mining Techniques. In: Grouping Multidimensional Data, pp. 25–71 (2006)
Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Communications of the ACM 18(11), 613–620 (1975)
Manning, C.D., Raghavan, P., Schütze, H.: An Introduction to Information Retrieval. Cambridge University Press, Cambridge (2009)
Salton, G., Buckley, C.: Term-weighting approaches in automatic retrieval. Information Processing & Management 24(5), 513–523 (1988)
WIPO - International Patent Classification, http://www.wipo.int/classifications/ipc/en/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vlase, M., Munteanu, D., Istrate, A. (2012). Improvement of K-means Clustering Using Patents Metadata. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2012. Lecture Notes in Computer Science(), vol 7376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31537-4_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-31537-4_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31536-7
Online ISBN: 978-3-642-31537-4
eBook Packages: Computer ScienceComputer Science (R0)