科技通报
科技通報
과기통보
BULLETIN OF SCIENCE AND TECHNOLOGY
2015年
6期
142-144
,共3页
非显著特征%数据挖掘%软件测试%数据库访问
非顯著特徵%數據挖掘%軟件測試%數據庫訪問
비현저특정%수거알굴%연건측시%수거고방문
non significant feature%data mining%software testing%database access
在软件故障测试和数据库访问中,对非显著特征数据的挖掘是难点,通过对非显著特征数据的挖掘,处理数据分布比较稀疏且呈现模式分布不规则的数据访问问题。提出一种基于链距离估计的非显著特征数据挖掘算法,在时域上对链距离估计模型进行平移处理,给出非显著特征数据的离群因子概念,提取关联度主特征量,基于链距离估计结果,得到有效特征挖掘概率密度值,实现对非显著特征数据挖掘算法改进。仿真实验表明,该算法使得无论是不同密度的点簇相互靠近还是出现模式偏离的情况,都能有效的挖掘出非显著特征点,从而增强了数据挖掘算法的有效性和通用性,采用该法能有效提高非显著特征数据的挖掘性能,数据挖掘的命中率较高,在数据库访问和软件故障测试等领域具有应用价值。
在軟件故障測試和數據庫訪問中,對非顯著特徵數據的挖掘是難點,通過對非顯著特徵數據的挖掘,處理數據分佈比較稀疏且呈現模式分佈不規則的數據訪問問題。提齣一種基于鏈距離估計的非顯著特徵數據挖掘算法,在時域上對鏈距離估計模型進行平移處理,給齣非顯著特徵數據的離群因子概唸,提取關聯度主特徵量,基于鏈距離估計結果,得到有效特徵挖掘概率密度值,實現對非顯著特徵數據挖掘算法改進。倣真實驗錶明,該算法使得無論是不同密度的點簇相互靠近還是齣現模式偏離的情況,都能有效的挖掘齣非顯著特徵點,從而增彊瞭數據挖掘算法的有效性和通用性,採用該法能有效提高非顯著特徵數據的挖掘性能,數據挖掘的命中率較高,在數據庫訪問和軟件故障測試等領域具有應用價值。
재연건고장측시화수거고방문중,대비현저특정수거적알굴시난점,통과대비현저특정수거적알굴,처리수거분포비교희소차정현모식분포불규칙적수거방문문제。제출일충기우련거리고계적비현저특정수거알굴산법,재시역상대련거리고계모형진행평이처리,급출비현저특정수거적리군인자개념,제취관련도주특정량,기우련거리고계결과,득도유효특정알굴개솔밀도치,실현대비현저특정수거알굴산법개진。방진실험표명,해산법사득무론시불동밀도적점족상호고근환시출현모식편리적정황,도능유효적알굴출비현저특정점,종이증강료수거알굴산법적유효성화통용성,채용해법능유효제고비현저특정수거적알굴성능,수거알굴적명중솔교고,재수거고방문화연건고장측시등영역구유응용개치。
In software fault testing and database access, mining of non significant features of the data is difficult, through the mining of non significant features of the data, processing data are very sparse and presented a model of the distribution of irregular data access issues. Put forward a kind of chain distance estimation based on non significant feature of data min?ing algorithms in time domain for distance estimation model for translational processing chain, are non significant features of the data outlier factor concept, extraction of association degree of the main features, the results estimated distance based on the chain, effectively feature mining probability density value, implementation of mining algorithm, improvement of non significant feature data. Simulation results show that the algorithm makes both the different density of the cluster are close to each other or model deviates from the situation, can efficiently discover the non obvious features, thereby enhancing the data mining algorithm is effective and versatile, can effectively improve the mining performance of non significant features of the data by using this method, data mining the hit rate is higher, it has application value in the database access and soft?ware fault testing and etc.