[1]XU Changlin,XU Hao.Similarity measurement method for normal cloud based on Hellinger distance and its application[J].CAAI Transactions on Intelligent Systems,2023,18(6):1312-1321.[doi:10.11992/tis.202209042]
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Similarity measurement method for normal cloud based on Hellinger distance and its application

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 6 Page number: 1312-1321 Column: 学术论文—人工智能基础 Public date: 2023-11-05
Title:
Similarity measurement method for normal cloud based on Hellinger distance and its application
Author(s):
XU Changlin1; 2;  XU Hao1
1. School of Mathematics and Information Science, North Minzu University, Yinchuan 750021, China;
2. The Key Laboratory of Intelligent Information and Big Data Processing of NingXia Province, North Minzu University, Yinchuan, 750021, China
Keywords:
knowledge representation; normal cloud; uncertainty; Hellinger distance; characteristic curve; similarity measurement; collaborative filtering; recommendation system
CLC:
TP311
DOI:
10.11992/tis.202209042
Abstract:
To address the problems of high computational complexity and weak discrimination of existing normal cloud model similarity measurement methods, a similarity measurement method for normal clouds based on Hellinger distance is proposed according to the characteristic curve of the normal cloud by taking inspiration from the similarity of two probability distributions described by Hellinger distance. The digital and distribution characteristics of the cloud concept were considered in the proposed method. Furthermore, the mathematical properties of the proposed similarity measurement were studied. Two similarity algorithms were then designed for the normal cloud concept on the basis of the given similarity measurement method. Finally, the performance of the proposed algorithms was compared and analyzed using numerical simulation and classification experiments on time-series data. Results showed that the proposed algorithms have good similarity discrimination capability, and their classification error rate and CPU time cost are low. Moreover, these algorithms were applied to the collaborative filtering recommendation system, and experiments were conducted on the MovieLens100k film review dataset. The experimental results revealed that the proposed methods can continue to achieve ideal recommendation quality even when the user rating data were extremely sparse.
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