¹Department of Business Administration, National Central University, Jhongli City, Taoyuan County 32001, Taiwan
²Department of Logistics Management, National Kaohsiung First University of Science and Technology, Kaohsiung City 811, Taiwan
³Institute of Management, National Kaohsiung First University of Science and Technology, Kaohsiung City 811, Taiwan

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Abstract—This research applied various data mining approaches to investigate the innovations of data processing technologies for inventory management based on the database of the United States Patent and Trademark Office. The first objective of data mining in this study is to find the core technologies by evaluating patent citation matrix and patent strength. This information can help companies to choose suitable tools through the understanding of the most essential innovations. A total of 63 core technologies were identified from 949 patents under the US patent class of 705/28. Besides, a network of patent development paths was also derived to illustrate the correlations of core advancements. Finally, this study adopted the method of nonhierarchical clustering analysis to identify key groups of technologies through the symmetrical matrix of relative correlation strength. Enterprise can refer the findings of clustering to recognize the trend and characteristics of data processing technologies for their strategic technology management.

Index Terms—data mining, patent analysis, inventory management, data processing

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Cite: Chien-Wen Shen, Heng-Chi Lee, Ching-Chih Chou, Chiao-Chun Cheng, "Data Mining the Data Processing Technologies for Inventory Management," Journal of Computers vol. 6, no. 4, pp. 784-791, 2011.