[1]戴立平,谭正华,张进修,等.一种改进的海量信息分类算法[J].计算机技术与发展,2019,29(10):201-204.[doi:10. 3969 / j. issn. 1673-629X. 2019. 10. 039]
　DAI Li-ping,TAN Zheng-hua,ZHANG Jin-xiu,et al.An Improved Algorithm for Classification of Massive Information[J].,2019,29(10):201-204.[doi:10. 3969 / j. issn. 1673-629X. 2019. 10. 039]
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一种改进的海量信息分类算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年10期
页码:
201-204
栏目:
智能、算法、系统工程
出版日期:
2019-10-10

文章信息/Info

Title:
An Improved Algorithm for Classification of Massive Information
文章编号:
1673-629X(2019)10-0201-04
作者:
戴立平; 谭正华; 张进修; 张又文
湘潭大学 信息工程学院,湖南 湘潭 411105
Author(s):
DAI Li-ping; TAN Zheng-hua; ZHANG Jin-xiu; ZHANG You-wen
School of Information Engineering,Xiangtan University,Xiangtan 411105,China
关键词:
海量信息; 二进制; 分类层级; 改进迭代算法
Keywords:
massive information; binary; classification level; improved iterative algorithm
分类号:
TP301.6
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 10. 039
摘要:
首先对信息分类过程中出现的问题进行了阐述,指出了传统信息分类方法的不足与缺陷,以及后续查询效率较低的问题等。 接着分析了普通的信息分类方法,主要包括了分类方式与查询方式,然后针对企业海量信息的查询效率较低及信息分类层级较少的问题,提出一种改进的海量信息分类算法。 采用 64 位二进制值作为信息分类编码,通过对分类编码的解析,能够准确并较为快速地分析出信息分类层次结构等信息。 该编码机制可以实现 8.59X1018 个分类。 将优化算法与传统算法进行了比较,实验结果表明:在分类层级较多的情况下,改进的迭代算法相较于普通的迭代算法在耗时上要少,且当分类层级和分类总数增加时,优化的分类算法在耗时增长速率上优于普通的迭代算法,提高了查询效率。
Abstract:
Firstly,the problems in the process of information classification are elaborated,and the shortcomings of traditional information classification methods,as well as the low efficiency of subsequent queries are also pointed out. Secondly,the common information classification methods are analyzed, including classification and query. Then, aiming at the problem of low query efficiency and less information classification hierarchy of enterprise massive information, an improved massive information classification algorithm is proposed. 64-bit binary value is used as information classification coding. The information like hierarchical structure of information clas sification can be worked out accurately and quickly by analyzing classification coding. The encoding mechanism can achieve 8.59X1018 categories. Comparing the optimized algorithm with traditional algorithm,the experiment shows that the improved iterative algorithm is less time-consuming than the ordinary iterative algorithm in the case of more classification hierarchies,and the optimized classification algorithm is better than the ordinary iterative algorithm in the time-consuming growth rate when the classification hierarchies and the total number of classifications increase. It also improves query efficiency.

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更新日期/Last Update: 2019-10-10