Using Patent Data For Technology Clusters Exploration: Quantum Dots Patent Analysis
Nurmitra Sari Purba (a), Rani Nooraeni (b)
a,b) STIS, Politechnic of Statistic,,East Jakarta, 13330, Indonesia
Abstract
Quantum dots (QDs) are colloidal semiconductor nanocrystals which have unique optical properties due to their three dimensional quantum confinement regime. According to MarketAndMarket report, the QDs market is estimated to grow from USD 2.57 Billion in 2018 to reach USD 8.47 Billion by 2023. The QDs market growth is mainly driven by the factors such as the increasing demand in high-quality display devices, growing implementation in numerous applications due to their miniature property, and rising adoption of energy-efficient and less or non-toxic in solar cells and photovoltaics. Based on data from 54 countries obtained from the World Bank, technology exports (US Dollar) have a positive and strong correlation with innovation variables, namely the number of journals and articles (0.8), resident patents (0.9) and non-resident patents (0.9). This study explores technology clusters through patent analysis. The aim of exploring technology clusters is to grasp competitors levels of sustainable research and development (R&D) and establish a sustainable strategy for entering an industry. 3914 patents are collected through scraping processes. This study uses the Text Statistics Mining method for text titles consisting of two phases: data preparation and data analysis phase. The data preparation phase uses the Text Mining method and the data analysis stage is done with statistics. Statistical analysis in this study used PCA combined with an unsupervised clustering algorithm, K-Means Clustering. The results of this study based on the Gap Statistics maximum value indicate 9 clusters are associated with fifteen terms in it can be used in information retrieval process.
Keywords: gap statistics, k-means++, pca, quantum dots, text mining
Topic: Business Strategies on GVC Participation