本文選題：決策樹 + 招聘　； 參考：《寧夏大學(xué)》2017年碩士論文

【摘要】：隨著計算機技術(shù)的快速發(fā)展,人們的生活變得便捷,工作效率也不斷提高,各行各業(yè)越來越多的數(shù)據(jù)被存儲下來。數(shù)據(jù)挖掘技術(shù)就是從原始的、不完整甚至存在錯誤的數(shù)據(jù)中發(fā)現(xiàn)數(shù)據(jù)之間的潛在聯(lián)系,然后從這些聯(lián)系中發(fā)現(xiàn)知識的過程。數(shù)據(jù)挖掘是多種學(xué)科相結(jié)合產(chǎn)生的,這些學(xué)科包括數(shù)據(jù)存儲的載體數(shù)據(jù)庫技術(shù),數(shù)據(jù)統(tǒng)計分析,機器學(xué)習(xí),神經(jīng)網(wǎng)絡(luò)等。本論文開始說明了人才對于企業(yè)生存和不斷發(fā)展的重要性,然后介紹了以銷售為主的企業(yè)對于銷售人才的大量需求,但是在銷售人才招聘的過程中,應(yīng)聘者自身往往不能在很短的時間內(nèi)表現(xiàn)出自身的優(yōu)點和缺點;人事部門由于時間或者能力有限等多種原因?qū)?yīng)聘者的判斷有可能存在著一定偏差,這給應(yīng)聘者和企業(yè)在招聘的過程中帶來了阻礙。與此同時企業(yè)存儲的大量的銷售員基本屬性信息得不到有效利用,于是本文決定采用數(shù)據(jù)挖掘分類技術(shù),利用企業(yè)現(xiàn)存的銷售員相關(guān)數(shù)據(jù)在應(yīng)聘者中篩選適合本公司銷售崗位的人才。然后介紹了數(shù)據(jù)挖掘中的分類方法,其中包括決策樹分類方法,樸素貝葉斯分類方法以及神經(jīng)網(wǎng)絡(luò)分類方法,并詳細(xì)介紹了ID3算法、CART算法、C4.5算法的原理以及決策樹剪枝的幾種方法,最后以北京某公司A區(qū)銷售員的相關(guān)數(shù)據(jù)為基礎(chǔ),對數(shù)據(jù)進(jìn)行預(yù)處理,運用R語言將CART算法和C4.5算法應(yīng)用到實驗數(shù)據(jù)中生成決策樹模型,并做出相關(guān)的分析。其中在CART算法中,根據(jù)節(jié)點的復(fù)雜程度對決策樹進(jìn)行剪枝,為的是優(yōu)化模型的同時盡量避免數(shù)據(jù)的過擬合現(xiàn)象的出現(xiàn)。最后比較了CART算法模型和C4.5算法模型的準(zhǔn)確度,利用C4.5算法生成的模型,實現(xiàn)了應(yīng)聘銷售員篩選系統(tǒng),為銷售企業(yè)銷售人員的招聘提供理論參考。本文最后對該研究做了總結(jié)與展望,說明了該模型存在的一些不足,以及改進(jìn)的方法。
[Abstract]:With the rapid development of computer technology, people's life becomes more convenient, and their working efficiency is improved. More and more data are stored in all walks of life. Data mining technology is the process of discovering the potential relationship between data from raw incomplete or even wrong data and then discovering knowledge from these connections. Data mining is a combination of a variety of disciplines, including data storage carrier database technology, data statistical analysis, machine learning, neural networks and so on. This paper begins with the importance of talent for the survival and continuous development of enterprises, and then introduces the large demand for sales talents in sales oriented enterprises, but in the process of recruiting sales talents, Candidates themselves are often unable to show their own advantages and disadvantages in a very short period of time. Personnel departments may have certain deviations in their judgment of candidates due to various reasons, such as time or limited ability. This creates obstacles for candidates and companies in the recruitment process. At the same time, a large number of basic attributes of salespeople stored in enterprises can not be used effectively, so this paper decides to adopt data mining and classification technology. Use the existing salesperson data in the company to select suitable candidates for the company's sales position. Then the classification methods in data mining are introduced, including decision tree classification, naive Bayes classification and neural network classification. The principle of C4.5 algorithm of ID3 algorithm and the pruning method of decision tree are introduced in detail. Finally, based on the relevant data of salesmen in A area of a company in Beijing, the data are preprocessed. The cart algorithm and C4.5 algorithm are applied to the experimental data to generate the decision tree model, and the related analysis is made. In cart algorithm, the decision tree is pruned according to the complexity of nodes in order to optimize the model and avoid the phenomenon of data over-fitting. Finally, the accuracy of cart algorithm model and C4.5 algorithm model is compared. The model generated by C4.5 algorithm is used to realize the candidate salesperson selection system, which provides a theoretical reference for the recruitment of sales personnel in sales enterprises. In the end, the paper summarizes and prospects the research, and explains some shortcomings of the model and the improved method.
【學(xué)位授予單位】：寧夏大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP311.13

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