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多层数据结构设计在K-means下的实现 被引量:2

Design of Multi-Levels Data Structure Based on K-means
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摘要 随着信息化时代的来临,数据量呈现直线上升,如何从这海量数据中提取有效信息是信息化发展必须解决的问题。传统数据结构设计采用数组、链表或者关联容器的数据结构设计方法,无法解决数据海量时带来的空间离散性和数据关联性问题,数据分析效率低。为此提出一种基于数据挖掘的点线面多层数据结构设计方法,采用K-means算法对数据进行挖掘,提取数据特征,然后设计了点、线、面多层数据结构,最后采用反距离插值的方法对数据的空间性能进行分析,并以实际空间离散数据为对象进行分析。结果显示,采用新设计的数据结构后,数据分析耗费时间更短,性能更加优越。 Study on the design of multiple levels data structure based on K-means algorithm. With the advent of its information time, the amount of data increased very quickly, and how to extract valid information from these massive amounts of data has become a serious problem. In traditional method, the data structure of arrays, linked lists or associative containers were used, so it can not solve the problem of space discrete and data correlation and it was inefficient. A design of three levels data structure was proposed to solve this problem, the K-means algorithm was used to do data mining and extract data features, the point, line, surface-layer data structure was designed, the ability was analyzed with algorithm of anti-range interpolation, and the actual data was taken as target, with much experiment, the new designed data structure used less time and it showed superior performance.
作者 虎治勤
出处 《科技通报》 北大核心 2014年第2期90-92,95,共4页 Bulletin of Science and Technology
关键词 数据结构 数据挖掘 K-MEANS算法 反距离插值 data structure data mining k-means algorithm anti-range interpolation
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