Using Patent Statistics and Principal Component Analysis to Predict Global Competition (pp. 45-50)

Francois A. Ravalison, Narisoa Rabenja
Globalization and acute competition require continuously searching new approach in identifying ways for better economic growth. The purpose of this paper is to identify a process to predict global competition. The data are collected from the Malagasy Intellectual Property Office. Frequencies of foreign patents, registered at that Office, from 1994 to 2009 have been collected. Then, data mining is conducted to bring out an idea. Findings reveal that Competition indicators based on patent statistics are confirmed as appropriate measure of competition. Then Principal Component Analysis (PCA), applied on Competition indicators, permits to predict sector of global competition in a country level. This paper serves as a valuable analytical framework for the management of patent data for continuous innovation for economic growth.

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