title: [Classification of 365 Chinese medicines in Shennong's Materia Medica Classic based on a semi-supervised incremental clustering method].
abstract: Evidence of the pharmacological activity of traditional Chinese medicine (TCM) provides the basis for clinical prescription. Study of the classification of Chinese medicines according to these activities is key to understanding the general active tendencies of medicinal prescriptions, exploring their material basis, investigating their properties and searching for their alternatives. Taking the herbal classic Shennong's Materia Medica Classic (Shennong Bencao Jing) for the data source, this paper studied the classification of Chinese medicines based on semi-supervised incremental clustering algorithm using "micro-cluster" concept in order to investigate the complex similarity among Chinese medicines. The results showed that 253 Chinese medicines were reasonably classified into 14 types, such as invigoration, clearing heat, diuresis, dredging blockages in the channels, treating gynecological conditions and treating strange diseases caused by ghosts. The results also showed that the other 112 Chinese medicines were classified into 112 individual types and the same high similarity to different known types was the main reason for this. The semi-supervised incremental clustering algorithm employed in the study had a high quality and a good development for clustering which is suitable for classification of Chinese medicines. This study illustrated the diversity of Chinese medicines and their complex similarities, thus aiming to provide innovative ideas and methods for related research.
authors.affiliation: School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100102, China.
authors.forename: Rui
authors.initials: R
authors.lastname: Jin
authors.forename: Bing
authors.initials: B
authors.lastname: Zhang
authors.forename: Chun-miao
authors.initials: CM
authors.lastname: Xue
authors.forename: Sen-mao
authors.initials: SM
authors.lastname: Liu
authors.forename: Qian
authors.initials: Q
authors.lastname: Zhao
authors.forename: Kang
authors.initials: K
authors.lastname: Li
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D004365:Drugs, Chinese Herbal / Q000145:classification*
chemical_list: D004365:Drugs, Chinese Herbal