本文選題：無(wú)線網(wǎng)絡(luò) + 特征提取��；參考：《計(jì)算機(jī)仿真》2016年11期

【摘要】：無(wú)線網(wǎng)絡(luò)中興趣數(shù)據(jù)的檢測(cè),可以改善用戶的網(wǎng)絡(luò)體驗(yàn)。對(duì)興趣數(shù)據(jù)檢測(cè)時(shí),首先在一定尺度下對(duì)無(wú)線網(wǎng)絡(luò)產(chǎn)生的數(shù)據(jù)進(jìn)行分解,分解后將用戶檢索頻率組分不同于其它大部分?jǐn)?shù)據(jù)的數(shù)據(jù)作為興趣數(shù)據(jù)。而傳統(tǒng)的葉貝斯算法,沒(méi)有明確的分解系數(shù),只能對(duì)整體數(shù)據(jù)進(jìn)行檢索,導(dǎo)致興趣數(shù)據(jù)檢測(cè)精度差的問(wèn)題。提出一種相關(guān)子空間的無(wú)線網(wǎng)絡(luò)中興趣數(shù)據(jù)優(yōu)化檢測(cè)方法。先計(jì)算局部數(shù)據(jù)屬性維上數(shù)據(jù)稀疏程度,利用計(jì)算的結(jié)果對(duì)與興趣數(shù)據(jù)相關(guān)子空間進(jìn)行定義,映射所有局部數(shù)據(jù)集上的興趣數(shù)據(jù)分布特征,并對(duì)興趣數(shù)據(jù)序列樣本進(jìn)行多維空間構(gòu)建,獲取興趣數(shù)據(jù)聚類間距距離,并作為依據(jù)實(shí)現(xiàn)無(wú)線網(wǎng)絡(luò)中興趣數(shù)據(jù)的優(yōu)化檢測(cè)。仿真結(jié)果表明,改進(jìn)算法可以提高無(wú)線網(wǎng)絡(luò)中興趣數(shù)據(jù)檢測(cè)效率,有效的提升興趣數(shù)據(jù)檢測(cè)的準(zhǔn)確性。
[Abstract]:The detection of interest data in wireless networks can improve the network experience of users.In the detection of interest data, the data generated by wireless network is decomposed at a certain scale, and then the data which is different from most other data is used as interest data.However, the traditional Bayesian algorithm has no explicit decomposition coefficient and can only retrieve the whole data, which leads to the problem of poor detection accuracy of the interesting data.An optimal detection method of interest data in wireless networks based on correlation subspace is proposed.Firstly, the sparse degree of local data attribute dimension is calculated, then the subspace related to interest data is defined by the result of calculation, and the distribution characteristics of interest data on all local data sets are mapped.The interest data sequence samples are constructed in multi-dimensional space to obtain the distance between the interest data clustering and as the basis to realize the optimal detection of interest data in wireless networks.Simulation results show that the improved algorithm can improve the efficiency of interest data detection and improve the accuracy of interest data detection.
【作者單位】： 廣州大學(xué)華軟軟件學(xué)院;
【分類號(hào)】：TN92

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