【摘要】：傳統(tǒng)的房屋評(píng)估方法,比如收益法、成本法、市場比較法等方法多存在成本高、效率低、精度差等各項(xiàng)問題。本論文基于對(duì)KNN算法和二手房評(píng)估的研究,分析了 KNN算法的特點(diǎn)以及將算法用于二手房評(píng)估上的可行性。二手房信息中的關(guān)鍵數(shù)據(jù)容易數(shù)值化和標(biāo)準(zhǔn)化,使用KNN算法在模型上可行;經(jīng)過篩選后的樣本集規(guī)�？煽�,KNN算法在時(shí)間復(fù)雜度上優(yōu)化空間也很大,在計(jì)算效率上可行。對(duì)于二手房信息這類結(jié)構(gòu)較清晰的數(shù)據(jù),KNN算法較易于實(shí)現(xiàn),成本較低,在經(jīng)濟(jì)上可行。本論文對(duì)數(shù)據(jù)挖掘中的分類技術(shù)和回歸技術(shù)進(jìn)行分析,之后選取KNN算法為對(duì)二手房進(jìn)行評(píng)估的核心技術(shù),實(shí)現(xiàn)了一個(gè)B/S(Browser/Server,瀏覽器/服務(wù)器模式)估價(jià)應(yīng)用,給予有二手房評(píng)估需求的目標(biāo)用戶一種快速獲得評(píng)估結(jié)果的方式。經(jīng)研究分析,經(jīng)典KNN算法具有精度較高、對(duì)樣本集中的噪聲不敏感等優(yōu)點(diǎn),也有k值難以選取、時(shí)間復(fù)雜度高、受樣本平衡度的影響大等缺點(diǎn)。本著揚(yáng)長避短的思想,對(duì)于算法優(yōu)點(diǎn),使用結(jié)果集加權(quán)的方式進(jìn)一步提高算法精度,使用去重和標(biāo)準(zhǔn)化等方式減少噪聲;對(duì)于算法缺點(diǎn),使用多次檢驗(yàn)法選定k值,使用TopK算法以及多線程并發(fā)的方式降低時(shí)間復(fù)雜度,使用數(shù)據(jù)采集階段就對(duì)數(shù)據(jù)進(jìn)行分類的方式穩(wěn)定樣本平衡度。為了對(duì)改進(jìn)的KNN算法進(jìn)行實(shí)用性驗(yàn)證,利用該算法對(duì)哈爾濱市的部分二手房數(shù)據(jù)進(jìn)行分析,通過對(duì)數(shù)據(jù)的預(yù)處理,基于改進(jìn)KNN算法的實(shí)現(xiàn)等步驟,最終給出二手房的估價(jià)結(jié)果。對(duì)于二手房業(yè)主及中介商等目標(biāo)用戶,使用改進(jìn)KNN算法進(jìn)行二手房評(píng)估的B/S應(yīng)用對(duì)比傳統(tǒng)的二手房評(píng)估方式,具有計(jì)算速度快、交互界面友好易用的優(yōu)勢(shì),很好的滿足了目標(biāo)用戶的需求。
[Abstract]:Traditional methods of house evaluation, such as income method, cost method and market comparison method, have many problems, such as high cost, low efficiency, poor precision and so on. Based on the research of KNN algorithm and second-hand house evaluation, this paper analyzes the characteristics of KNN algorithm and the feasibility of applying the algorithm to second-hand housing evaluation. The key data in second-hand housing information is easy to be numerical and standardized, and the KNN algorithm is feasible in the model, and the filtered sample set size controllable KNN algorithm also has a large space in time complexity optimization, and is feasible in computing efficiency. The KNN algorithm is easy to realize for the second-hand housing information, which has a clear structure, and the cost is lower, so it is economically feasible. This paper analyzes the classification technology and regression technology in data mining, then selects KNN algorithm as the core technology to evaluate second-hand housing, and realizes a B / S (browser / Server mode) evaluation application. Give target users with second-hand housing assessment needs a quick way to get results. Through research and analysis, it is found that the classical KNN algorithm has the advantages of high precision, insensitivity to the noise in the sample set, and the disadvantages of hard to select k value, high time complexity and large influence of sample balance. For the advantages of the algorithm, the method of weighted result set is used to further improve the accuracy of the algorithm, and the method of de-duplication and standardization is used to reduce the noise, and for the shortcomings of the algorithm, the value of k is selected by the method of multiple tests. The time complexity is reduced by using TopK algorithm and multi-thread concurrency, and the sample balance is stabilized by classifying the data in the data acquisition stage. In order to verify the practicability of the improved KNN algorithm, this algorithm is used to analyze some second-hand housing data in Harbin. Through the preprocessing of the data and the realization of the improved KNN algorithm, the evaluation results of the second-hand house are given. For the target users such as second-hand house owners and intermediaries, the B / S application of the improved KNN algorithm for second-hand housing evaluation has the advantages of faster calculation speed and friendly interface than the traditional second-hand housing evaluation method. Meet the needs of the target users well.
【學(xué)位授予單位】：哈爾濱商業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F299.23;TP311.13

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