[1]夏达,李士进.水质时间序列模式挖掘[J].计算机技术与发展,2018,28(05):149-153.[doi:10．3969/j．issn．1673－629X．2018．05．034]
　XIA Da,LI Shijin.Pattern Mining for Water Quality Time Series[J].,2018,28(05):149-153.[doi:10．3969/j．issn．1673－629X．2018．05．034]
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水质时间序列模式挖掘()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年05期
页码:
149-153
栏目:
应用开发研究
出版日期:
2018-05-10

文章信息/Info

Title:
Pattern Mining for Water Quality Time Series
文章编号:
1673－629X(2018)05－0149－05
作者:
夏达; 李士进
河海大学 计算机与信息学院，江苏 南京 210098
Author(s):
XIA Da; LI Shi－jin
School of Computer and Information，Hohai University，Nanjing 210098，China
关键词:
数据挖掘; 序列模式挖掘; 间隔约束; One－Off 条件
Keywords:
data mining; sequential pattern mining; gap constraints; One－Off condition
分类号:
TP311
DOI:
10．3969/j．issn．1673－629X．2018．05．034
文献标志码:
A
摘要:
对水质时间序列进行数据挖掘，找出其蕴含的模式，对于水资源的改善有重要的现实意义。针对带间隔约束的有序时间序列的模式挖掘，现有算法多按左优先匹配以完备性为代价加快效率或枚举可能位置损失效率提高完备性。为了提高模式挖掘的效率同时保证一定的完备性，提出一种满足 One－Off 条件的带有间隔约束的单序列模式挖掘算法 FOFM(fast one－offing mining)。算法首先扫描序列获得长度为 1 的模式，再通过将当前长度的所有频繁模式进行两两比较，而后连接可连接的模式以形成新的模式，在模式连接的过程中记录候选模式最后事件的可能位置并通过回溯位置序列的方法检查模式的支持度，直至无法生成新的模式。实验结果表明，FOFM 算法在水质时间序列上相较于相关序列模式挖掘算法拥有较高的效率和一定的完备性。
Abstract:
It’s important to mine water quality time series and find out its pattern in series for the improvement of water resources．For the mining of time series with interval constraints，most of existing algorithms reduce the completeness by left－first matching for the efficiency or reduce the efficiency by enumerating the possible position for the completeness．In order to improve the efficiency of pattern mining and maintain a high degree of completeness，we propose a fast one－offing mining algorithm with One－off condition and gap constraints．
It first scans the sequence to obtain the pattern of length 1，and then obtains the candidate pattern through comparison on all frequent patterns of the current length after connection of the pattern．The possible position of the last event of the candidate pattern during the pattern connection is recorded and the support of the pattern by the backtracking sequence is checked，until a new pattern can’t be generated．The experiment based on the water quality time series proves that the FOFM is more effective than the related sequential pattern mining algorithm with a certain completeness．

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更新日期/Last Update: 2018-07-06