Abstract
Patents have become a potentially powerful data sources and a wealth of information for companies and organizations that tend to analyze and exploit them for a variety of purposes and interests. However, with the ever-increasing volume of patents filed year after year and the multiplicity of patent global data bases, the task of patent research and analysis is becoming increasingly complicated and traditional analytical approaches have shown their limitations and have become costly in terms of time and labor. Thus, various techniques and approaches have been proposed to help specialists in their tasks of patent collection, analysis and results visualization. Document Clustering is one of these common technics widely used in patent analysis. Over time, several powerful clustering techniques and algorithms have emerged, but they often require modifications and adaptations depending on the fields of application and the target data. In view of this, we propose in this paper a methodology for obtaining an efficient clustering for patent documents based on the k-means, k-means ++ algorithm and various data-mining and text-mining techniques. Commons issues often faced during the analysis of patents such as the manipulation and representation of textual data or the curse of dimension will also be addressed in this study.
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References
Zhang, L.: Identifying key technologies in Saskatchewan, Canada: evidence from patent information. World Pat. Inf. 33(4), 364–370 (2011). https://doi.org/10.1016/j.snb.2011.07.062
Pilkington, A., Lee, L.L., Chan, C.K., Ramakrishna, S.: Defining keyinventors: a comparison of fuel cell and nanotechnology industries. Technol. Forecast. Soc. Change 76(1), 118–127 (2009). https://doi.org/10.1016/j.techfore.2008.03.015
Thorleuchter, D., Poel, D.V., Prinzie, A.: A compared R&D-based and patent-based cross impact analysis for identifying relationships between technologies. Technol. Forecast. Soc. Change 77(7), 1037–1050 (2010). https://doi.org/10.1016/j.techfore.2010.03.002
Wang, Y.L., Huang, S., Wu, Y.C.J.: Information technology innovation in India: the top 100 IT firms. Technol. Forecast. Soc. Change 79(4), 700–708 (2012). https://doi.org/10.1016/j.techfore.2011.10.009
Abraham, B.P., Moitra, S.D.: Innovation assessment through patent analysis. Technovation 21(4), 245–252 (2001). https://doi.org/10.1016/j.techfore.2011.10.009
Trappey, A.J.C., Trappey, C.V., Wu, C.Y., Lin, C.W.: A patent quality analysis for innovative technology and product development. Adv. Eng. Inform. 26(1), 26–34 (2012). https://doi.org/10.1016/j.aei.2011.06.005
Porter, A.L., Cunningham, S.W.: Tech Mining: Exploiting New Technologies for Competitive Advantage. Wiley, Hoboken (2005). ISBN 978-0-471-47567-5
Allison, J., Lemley, M., Moore, K., Trunkey, R.: Valuable Patents. Berkeley Olin Program in Law & Economics Working Paper Series (2003)
Allison, J., Lemley, M., Walker, J.: Extreme value or trolls on top? The characteristics of the most litigated patents. Univ. Pa. Law Rev. 158(1), 1–37 (2009)
Kukolj, D., Tekic, Z., Nikolic, L.J., Panjkov, Z., Pokric M., Drazic, M., Vitas, M., Nemet, D.: Comparison of algorithms for patent documents clusterization. In: Proceedings of 35th MIPRO, Opatija, MIPRO 2012, pp. 1176–1178 (2012). ISBN: 978-953-233-068-7
Kim, Y.G., Suh, J.H., Park, S.C.: Visualization of patent analysis for emerging technology. Expert Syst. Appl. Int. J. 34, 1804–1812 (2008)
Jun, S., Park, S.-S., Jang, D.-S.: Document clustering method using dimension reduction and support vector clustering to overcome sparseness. Expert Syst. Appl. 41, 3204–3212 (2014)
Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill Inc., New York (1986)
Aizawa, A.: An information-theoretic perspective of tf–idf measures. Inf. Process. Manage. Int. J. 39, 45–65 (2003)
Salton, G., Buckley, C.: Weighting approaches in automatic text retrieval. Inf. Process. Manage. 24(5), 513–523 (1988)
WIPO - International Patent Classification: http://www.wipo.int/classifications/ipc/en/
Arthur, D., Vassilvitskii, S.: K-means ++: the advantages of careful seeding. In: Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2007, pp. 1027–1035. Society for Industrial and Applied Mathematics, Philadelphia (2007)
Huang, Z.: Clustering Large datasets with mixed Numeric and Categorical Values. CSIRO Mathematical and Information Sciences, Australia (1997)
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El Khammal, A., En-Naimi, E.M., Kanas, M., El Bouhdidi, J., El Haddadi, A. (2018). Automatics Tools and Methods for Patents Analysis: Efficient Methodology for Patent Document Clustering. In: Ben Ahmed, M., Boudhir, A. (eds) Innovations in Smart Cities and Applications. SCAMS 2017. Lecture Notes in Networks and Systems, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-319-74500-8_4
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DOI: https://doi.org/10.1007/978-3-319-74500-8_4
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