Sample classification algorithm based on spectral similarity calculation
Hits:
Release time:2023-03-16
Journal:Advanced Materials Research
Key Words:spectral similarity calculation; field geological sample classification; cluster analysis algorithm; spectra sort encoding algorithm
Abstract:According to the field geological sample classification, a spectral similarity the cluster analysis algorithm (SSCA) has been put forward. This algorithm expands and improves the spectra sort encoding algorithm on the spectral sorting and similarity computation, and adds similarity clustering analysis method. Testing on 100 field geological samples using SSCA algorithm, we get the results showing that this algorithm can make further classification for field geological samples to some extent.
Co-author:Qiu Lan, Wu Qianhong, Wang Ying
First Author:Deng Jiqiu
Indexed by:Journal paper
Document Type:J
Issue:490-495
Page Number:568-572
Translation or Not:no
Date of Publication:2012-09-01
Included Journals:EI
-
|
Zip Code:c576f0c85848a14d10428d1b79269fc7d68bde9ba6ecb95648b168a03671c3821c778c2bedf0cae5d03c95a065c3a5efa4243241acb9eb6089885db6f9f3fa7f5b7491eb5af48e7ab43d253e29e0fa6991b0af260ac98903cc7fbca150cb2fc86cb6bd41d95f78a263ba3d6f6aa504ee56cdd8693f2e6f13b576940cee3633de
Postal Address:b8aa5f788763ab7b115d9f4a2f5775cc0dec4df3d1f3c99f00943bec73bb76a8ab1071ab6e7b703b4d29611fe78e5af4b9f5ef666e69fa87eabd44d4423899c62128523356b51b88864f8fbf1f0eedf9f51049b1ff09b5382f8711772f0676fd8391ca14bf45bce2ddb0e92e640a2575c1a0e206a262b5d05d30bff7d4cd4559
Mobile:95863e09035286dda21e7c015a9a30e61699b949e5e8f79ea5799e29d2e8538e7023e78b5741bac8199489409b3d640bf8ea5aa0e82d3ecbe37b8896509d5381af491d379548ea34e0ccf2a8c34c89bfeaf92847f74d6a785735085a5d8b3a97c6ea43179c2d58a8034e458b52a3ba80a8d02454857ce6f40e324f878a098c33
Email:92be96ccd7bb71d47f9f10df47629670af270a59331d108e8c3b01ef1af33ce92c14b7aff0d30c1cbf2a121e06e3104723da9adae625923ecd3fe7d7b9873e8d1886c3fffb116663e97a57ff6a538d209d5c0bfe12c2b073e83f940ae41036b26f756f0e93155e7426bdf0c59631c0bc2de324875ec962f47a170ff93d81e7cc
|