Paper Info
Title
A Hybrid Model Combining Soms With Svrs For Patent Quality Analysis And Classification
Abstract
Traditional researchers and analyzers have fixated on developing sundry patent quality indicators only, but these indicators do not have further prognosticating power on incipient patent applications or publications. Therefore, the data mining (DM) approaches are employed in this paper to identify and to classify the new patent's quality in time. An automatic patent quality analysis and classification system, namely SOM-KPCA-SVM, is developed according to patent quality indicators and characteristics, respectively. First, the model will cluster patents published before into different quality groups according to the patent quality indicators and defines group quality type instead of via experts. Then, the support vector machine (SVM) is used to build up the patent quality classification model. The proposed SOM-KPCA-SVM is applied to classify patent quality automatically in patent data of the thin film solar cell. Experimental results show that our proposed system can capture the analysis effectively compared with traditional manpower approach.
Year
DOI
Venue
2016
10.1007/978-3-319-40973-3_26
DATA MINING AND BIG DATA, DMBD 2016
Keywords
Field
DocType
Patent analysis, Patent quality, Data clustering, Patent quality classification, Machine learning
Data mining,Thin film solar cell,Computer science,Support vector machine,Artificial intelligence,Patent analysis,Cluster analysis,Machine learning
Conference
Volume
ISSN
Citations 
9714
0302-9743
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Pei-Chann Chang11752109.32
Jheng-Long Wu2959.54
Cheng-Chin Tsao381.89
Chin-Yuan Fan447328.27