本文選題：數(shù)據(jù)挖掘 + 關(guān)聯(lián)規(guī)則��； 參考：《沈陽理工大學(xué)》2017年碩士論文

【摘要】：網(wǎng)絡(luò)縮短了世界的距離,作用日益重要,網(wǎng)絡(luò)故障的影響也越來越嚴(yán)重。傳統(tǒng)的網(wǎng)絡(luò)故障診斷方法主要依靠工作人員在長期處理故障的過程中所積累的經(jīng)驗(yàn),這種方法效率較低且培養(yǎng)一個(gè)有經(jīng)驗(yàn)的技術(shù)人員花費(fèi)的成本較高、培養(yǎng)周期長。隨著數(shù)據(jù)挖掘技術(shù)的逐漸成熟及在社會生活中的成功應(yīng)用,將其應(yīng)用于網(wǎng)絡(luò)故障告警相關(guān)性分析也成為研究的熱點(diǎn)。分析故障告警之間相互關(guān)聯(lián)的規(guī)則并應(yīng)用于網(wǎng)絡(luò)故障定位,能夠提高故障定位的效率,具有很高的實(shí)際應(yīng)用價(jià)值。本課題以數(shù)據(jù)挖掘技術(shù)和可拓學(xué)知識為基礎(chǔ),以通信網(wǎng)絡(luò)故障告警相關(guān)性分析及故障點(diǎn)定位為目的,完成可拓關(guān)聯(lián)規(guī)則應(yīng)用于告警相關(guān)性分析的理論算法研究,并通過合成數(shù)據(jù)集,運(yùn)用C#語言進(jìn)行編程做仿真實(shí)驗(yàn),通過Matlab軟件作圖,以圖形化的形式進(jìn)行數(shù)據(jù)分析,驗(yàn)證算法的合理性。首先,詳細(xì)介紹基于Apriori算法的網(wǎng)絡(luò)故障告警相關(guān)性分析實(shí)例,分析此算法及其改進(jìn)算法在告警相關(guān)性分析中存在的瓶頸。然后,在此基礎(chǔ)上根據(jù)網(wǎng)絡(luò)故障告警數(shù)據(jù)的特點(diǎn)提出本課題的算法。根據(jù)可拓學(xué)的基元理論以及告警屬性的特征,采用可拓物元模型對告警信息中的關(guān)鍵屬性進(jìn)行形式化表達(dá),使其適用于數(shù)據(jù)挖掘算法并且從根源上減少數(shù)據(jù)量;在添加水平權(quán)值的基礎(chǔ)上,根據(jù)告警信息的時(shí)間特性,添加垂直權(quán)值,這種混合加權(quán)算法可以減少冗余規(guī)則的產(chǎn)生,提高故障診斷的效率;在設(shè)置水平權(quán)重系數(shù)的過程中為減少主觀因素的影響,根據(jù)告警實(shí)例屬性的特征,考慮各屬性值對水平權(quán)值的客觀作用,運(yùn)用可拓關(guān)聯(lián)函數(shù)的方法計(jì)算告警之間的相似度并確定權(quán)重值。為減少對告警實(shí)例庫的掃描次數(shù),在關(guān)聯(lián)規(guī)則挖掘過程中,采用集合的方式存儲項(xiàng)集,能夠有效降低時(shí)間復(fù)雜度。通過仿真實(shí)驗(yàn),驗(yàn)證算法的合理性。最后,分析網(wǎng)絡(luò)故障實(shí)時(shí)診斷的必要性及研究意義。根據(jù)可拓關(guān)聯(lián)規(guī)則的特點(diǎn),結(jié)合可拓距與可拓變換理論提出新發(fā)告警與可拓關(guān)聯(lián)規(guī)則的匹配方法。
[Abstract]:Network shortens the distance of the world, plays an increasingly important role, and the impact of network failures is becoming more and more serious. The traditional network fault diagnosis method mainly relies on the experience accumulated by the staff during the long period of dealing with the fault. The efficiency of this method is low and the cost of training an experienced technician is high and the training period is long. With the maturity of data mining technology and its successful application in social life, the application of data mining technology to network fault alarm correlation analysis has become a hot topic. It can improve the efficiency of fault location by analyzing the correlation rules of fault alarm and applying it to network fault location, which has high practical application value. Based on the technology of data mining and extension knowledge, and aiming at the correlation analysis of fault alarm and the location of fault point in communication network, the research on the theoretical algorithm of applying extension association rules to alarm correlation analysis is completed in this paper. The simulation experiment is carried out by using C # language, and the data is analyzed graphically by Matlab software, and the rationality of the algorithm is verified by synthesizing the data set and using C # language to do the simulation experiment. Firstly, an example of network fault alarm correlation analysis based on Apriori algorithm is introduced in detail, and the bottleneck of this algorithm and its improved algorithm in alarm correlation analysis is analyzed. Then, according to the characteristics of network fault alarm data, the algorithm of this topic is proposed. According to the basic theory of extenics and the characteristics of alarm attributes, the key attributes in alarm information are formally expressed by extension matter-element model, which is suitable for data mining algorithms and reduces the data volume. On the basis of adding horizontal weights and adding vertical weights according to the time characteristics of alarm information, this hybrid weighting algorithm can reduce the generation of redundant rules and improve the efficiency of fault diagnosis. In order to reduce the influence of subjective factors in setting the horizontal weight coefficient, the objective effect of each attribute value on the level weight value is considered according to the characteristics of alarm instance attribute. The method of extension correlation function is used to calculate the similarity between alarms and determine the weight value. In order to reduce the number of scans on the alarm case library, in the process of mining association rules, the item set can be stored in the form of set, which can effectively reduce the time complexity. The rationality of the algorithm is verified by simulation experiments. Finally, the necessity and significance of real-time network fault diagnosis are analyzed. According to the characteristics of extension association rules, combined with extension distance and extension transformation theory, the matching method of new alarm and extension association rules is proposed.
【學(xué)位授予單位】：沈陽理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP311.13;TP393.06

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