發(fā)布時(shí)間：2018-03-26 01:29

  本文選題：大數(shù)據(jù)　切入點(diǎn)：數(shù)據(jù)挖掘　出處：《合肥工業(yè)大學(xué)》2017年碩士論文

【摘要】：隨著社會(huì)政治、經(jīng)濟(jì)和科技的高速發(fā)展,犯罪事件也以一定的速率不斷增長(zhǎng),而且違法犯罪更具組織化、職業(yè)化和高智能化。我國(guó)公安信息系統(tǒng)信息化程度不高,分析研判不夠智能化,決策機(jī)制有失科學(xué)性,缺乏對(duì)數(shù)據(jù)由宏觀到微觀的問題發(fā)現(xiàn)手段,如何利用數(shù)據(jù)挖掘的相關(guān)技術(shù),充分發(fā)揮警務(wù)大數(shù)據(jù)的價(jià)值和作用,使其運(yùn)用到警務(wù)工作中,提高執(zhí)法效率和預(yù)防打擊犯罪活動(dòng),已經(jīng)成為公安信息化建設(shè)中急需解決的問題。因此本文針對(duì)大數(shù)據(jù)環(huán)境下,公安技術(shù)應(yīng)用不足、備選嫌疑人眾多而預(yù)測(cè)方法相對(duì)落后的問題,提出了運(yùn)用支持向量機(jī)(SVM)預(yù)測(cè)犯罪嫌疑人的方法,提高偵破效率。傳統(tǒng)的嫌疑人預(yù)測(cè)方法大都通過回歸或者分類方法,對(duì)嫌疑人的可能性進(jìn)行判斷,這可能會(huì)導(dǎo)致錯(cuò)判的可能性。針對(duì)這一問題,本文對(duì)嫌疑人的特征進(jìn)行預(yù)測(cè),提出基于支持向量機(jī)的一種新穎的嫌疑人特征預(yù)測(cè)方法。首先,本文對(duì)支持向量機(jī)的基本原理進(jìn)行介紹,在其基礎(chǔ)上提出嫌疑人特征預(yù)測(cè)模型,并通過實(shí)驗(yàn)驗(yàn)證模型的有效性,針對(duì)大數(shù)據(jù)環(huán)境下嫌疑人特征預(yù)測(cè)問題,提出基于Hadoop的分布式嫌疑人特征預(yù)測(cè)框架。本文的研究成果主要有以下幾個(gè)方面:(1)針對(duì)問題特性以及支持向量機(jī)的特點(diǎn),將支持向量機(jī)算法運(yùn)用到嫌疑人預(yù)測(cè)問題中。(2)提出嫌疑人特征預(yù)測(cè)模型。首先對(duì)數(shù)據(jù)進(jìn)行預(yù)處理,并采用信息增益的特征選擇方法進(jìn)行特征選擇,基于支持向量機(jī)構(gòu)建嫌疑人特征預(yù)測(cè)模型,運(yùn)用粒子群算法(PSO)對(duì)模型的參數(shù)進(jìn)行優(yōu)化,并通過實(shí)驗(yàn)對(duì)模型進(jìn)行評(píng)估,驗(yàn)證其可行性。(3)提出基于Hadoop的分布式嫌疑人特征預(yù)測(cè)框架,解決海量數(shù)據(jù)嫌疑人特征預(yù)測(cè)問題。設(shè)計(jì)案件特征選擇的并行化和分布式SVM的運(yùn)行,并于單機(jī)的SVM進(jìn)行對(duì)比實(shí)驗(yàn)分析,驗(yàn)證了Hadoop處理效率更高。本文的研究成果,不僅較好的解決了嫌疑人預(yù)測(cè)問題,也為嫌疑人預(yù)測(cè)、協(xié)助辦案并提高辦案效率提供了新的思路,具有一定的實(shí)際意義和借鑒價(jià)值。
[Abstract]:With the rapid development of social politics, economy and science and technology, the crime has been increasing at a certain rate, and the crime is more organized, professional and intelligent. Analysis and judgment is not intelligent, decision-making mechanism is not scientific, lack of data from macro to micro problem discovery means, how to make use of data mining related technology, give full play to the value and role of police big data, It has become an urgent problem in the information construction of public security to apply it to police work, to improve the efficiency of law enforcement and to prevent and crack down on criminal activities. Therefore, this paper aims at the insufficient application of public security technology in the environment of big data. In order to improve the detection efficiency, this paper puts forward the method of using support vector machine (SVM) to predict the criminal suspect, which is relatively backward in the prediction methods of many alternative suspects. Most of the traditional suspect prediction methods adopt regression or classification methods. Judging the possibility of suspect, this may lead to the possibility of misjudgment. In view of this problem, this paper predicts the feature of suspect, and puts forward a novel method of suspect feature prediction based on support vector machine. This paper introduces the basic principle of support vector machine, puts forward the suspect feature prediction model on the basis of it, and proves the validity of the model through experiments, aiming at the suspect feature prediction problem under big data environment. This paper proposes a framework for feature prediction of distributed suspects based on Hadoop. The main research results of this paper are as follows: 1) aiming at the characteristics of the problem and the characteristics of support vector machines, The support vector machine (SVM) algorithm is applied to the suspect prediction problem. (2) A suspect feature prediction model is proposed. Firstly, the data is preprocessed, and the feature selection method based on information gain is used to select the feature. Based on the support vector mechanism (SVM), the suspect feature prediction model is built, and the parameters of the model are optimized by particle swarm optimization (PSO), and the feasibility of the model is verified by experiments. (3) A distributed suspect feature prediction framework based on Hadoop is proposed. In order to solve the problem of suspect feature prediction in mass data, the parallelization of case feature selection and the operation of distributed SVM are designed, and compared with SVM on a single computer, it is verified that the efficiency of Hadoop processing is higher. It not only solves the problem of suspect prediction, but also provides a new way of thinking for suspect forecasting, assisting in handling cases and improving the efficiency of handling cases. It has certain practical significance and reference value.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：D917.6

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