TY - GEN
T1 - Frequent itemsets compressing based on minimum cover
T2 - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
AU - Zhang, Lei
AU - Wang, Yiguo
AU - Zhang, Qiming
AU - Zhou, Xuezhong
AU - Yu, Jian
AU - Guo, Xiuhua
AU - Li, Xia
PY - 2013
Y1 - 2013
N2 - Frequent itemsets mining is often used to find medication law from dataset of Chinese herb prescriptions. Threshold of support count is difficult to set for traditional algorithm of frequent itemsets mining. In the meantime, the number of frequent itemsets is always so big that the result is hard to understand. Some algorithms were proposed to find significant and redundant-aware itemsets. However, the itemsets obtained could not reflect all the information in the dataset. In this paper, a new method was proposed to obtain a collection of itemsets which had the feature of significant, redundant-aware and comprehensive. Firstly, closed frequent itemsets were mined from the dataset of Chinese herbs prescriptions using CHARM algorithm. Then, the itemsets were compressed by FICMC (Frequent Itemsets Compressing based on Minimum Cover) algorithm. Medication law of Chinese herbs could be fully mined from the dataset using this method.
AB - Frequent itemsets mining is often used to find medication law from dataset of Chinese herb prescriptions. Threshold of support count is difficult to set for traditional algorithm of frequent itemsets mining. In the meantime, the number of frequent itemsets is always so big that the result is hard to understand. Some algorithms were proposed to find significant and redundant-aware itemsets. However, the itemsets obtained could not reflect all the information in the dataset. In this paper, a new method was proposed to obtain a collection of itemsets which had the feature of significant, redundant-aware and comprehensive. Firstly, closed frequent itemsets were mined from the dataset of Chinese herbs prescriptions using CHARM algorithm. Then, the itemsets were compressed by FICMC (Frequent Itemsets Compressing based on Minimum Cover) algorithm. Medication law of Chinese herbs could be fully mined from the dataset using this method.
KW - Closed frequent itemsets
KW - Frequent itemsets mining
KW - Medication law of Chinese herbs
KW - Minimum cover
UR - http://www.scopus.com/inward/record.url?scp=84894552155&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2013.6732703
DO - 10.1109/BIBM.2013.6732703
M3 - Conference contribution
AN - SCOPUS:84894552155
SN - 9781479913091
T3 - Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
SP - 315
EP - 318
BT - Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
Y2 - 18 December 2013 through 21 December 2013
ER -