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

【摘要】：隨著社會政治、經(jīng)濟和科技的高速發(fā)展,犯罪事件也以一定的速率不斷增長,而且違法犯罪更具組織化、職業(yè)化和高智能化。我國公安信息系統(tǒng)信息化程度不高,分析研判不夠智能化,決策機制有失科學性,缺乏對數(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)的價值和作用,使其運用到警務(wù)工作中,提高執(zhí)法效率和預防打擊犯罪活動,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測方法相對落后的問題,提出了運用支持向量機(SVM)預測犯罪嫌疑人的方法,提高偵破效率。傳統(tǒng)的嫌疑人預測方法大都通過回歸或者分類方法,對嫌疑人的可能性進行判斷,這可能會導致錯判的可能性。針對這一問題,本文對嫌疑人的特征進行預測,提出基于支持向量機的一種新穎的嫌疑人特征預測方法。首先,本文對支持向量機的基本原理進行介紹,在其基礎(chǔ)上提出嫌疑人特征預測模型,并通過實驗驗證模型的有效性,針對大數(shù)據(jù)環(huán)境下嫌疑人特征預測問題,提出基于Hadoop的分布式嫌疑人特征預測框架。本文的研究成果主要有以下幾個方面:(1)針對問題特性以及支持向量機的特點,將支持向量機算法運用到嫌疑人預測問題中。(2)提出嫌疑人特征預測模型。首先對數(shù)據(jù)進行預處理,并采用信息增益的特征選擇方法進行特征選擇,基于支持向量機構(gòu)建嫌疑人特征預測模型,運用粒子群算法(PSO)對模型的參數(shù)進行優(yōu)化,并通過實驗對模型進行評估,驗證其可行性。(3)提出基于Hadoop的分布式嫌疑人特征預測框架,解決海量數(shù)據(jù)嫌疑人特征預測問題。設(shè)計案件特征選擇的并行化和分布式SVM的運行,并于單機的SVM進行對比實驗分析,驗證了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測問題,也為嫌疑人預測、協(xié)助辦案并提高辦案效率提供了新的思路,具有一定的實際意義和借鑒價值。
[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.
【學位授予單位】：合肥工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：D917.6

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本文編號：1665731