[1]WANG Feng,GU Jiaojiao,LIN Yu.Mining conducted knowledge by accumulating N active transformations[J].CAAI Transactions on Intelligent Systems,2019,14(5):1035-1039.[doi:10.11992/tis.201804042]
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Mining conducted knowledge by accumulating N active transformations

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 5 Page number: 1035-1039 Column: 学术论文—知识工程 Public date: 2019-09-05
Title:
Mining conducted knowledge by accumulating N active transformations
Author(s):
WANG Feng;  GU Jiaojiao;  LIN Yu
The Naval Aviation University, Yantai 264001, China
Keywords:
extension; extension transformation; data mining; conduction knowledge; sensitivity; weapon system stereotype; information element
CLC:
TJ760
DOI:
10.11992/tis.201804042
Abstract:
Considering the fact that the N extensive transformations do not cause conductive transformation of related information elements, and only N+1 active transformation can cause conductive transformation of related information element, in this study, extensive transformation and conduction effect are applied, and the conducted knowledge of such extensive transformations and conductive transformation is deeply mined by giving the concept of sensitivity of a certain feature of information elements related to target features. Through the test data analysis in the process of finalizing a missile weapon system, it is shown that this method is perfect and supplementary to the existing theory of data mining for conducted knowledge, and makes the theory of conducted knowledge mining more comprehensive.
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