Classification of Patents by Using the Text Mining Approach Based On PCA and Logistics
Manmeet Kaur1, Richa Sapra2
1Manmeet Kaur, Computer Science, Lovely Professional University, India
2Richa Sapra, Computer Science, Lovely Professional University, India.
Manuscript received on March 22, 2013. | Revised Manuscript received on April 15, 2013. | Manuscript published on April 30, 2013. | PP: 711-714 | Volume-2, Issue-4, April 2013. | Retrieval Number: D1514042413/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Analysis of patent data is important tool for industrial research. Patent analysis has been used in many research fields and applied for rich topics in technology management. Patents are often used as the source of inspiration for new ideas. Patents contain detailed technical information about technical problem and the preferred technical solution. This information can be used for example to assess the state of the art or as a basis to identify possible gaps in a technology field. But often it is a very time consuming process to analyze the information provided by patents, because huge amounts of patents have to be considered. This paper proposes an intelligent system for classification based on Principle component analysis (PCA) and logistics. The intelligent system is designed to extract the features from the patents database and classify them according to the predefined categories as software, biological, business and chemical. Three different stages are designed to classify the content of patents such as (1) text pre-processing (2) PCA based features extraction and (3) classification using logistics. The main advantage of this approach is that the user need not to read whole patent documents but able to retrieve the relevant parts of the text in short time for further analysis process.
Keywords: Classification, Data mining, Logistics, PCA, Text mining.