發(fā)布時間：2018-07-18 11:03

【摘要】：傳統(tǒng)的房屋評估方法,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、精度差等各項問題。本論文基于對KNN算法和二手房評估的研究,分析了 KNN算法的特點以及將算法用于二手房評估上的可行性。二手房信息中的關鍵數(shù)據(jù)容易數(shù)值化和標準化,使用KNN算法在模型上可行;經(jīng)過篩選后的樣本集規(guī)模可控,KNN算法在時間復雜度上優(yōu)化空間也很大,在計算效率上可行。對于二手房信息這類結構較清晰的數(shù)據(jù),KNN算法較易于實現(xiàn),成本較低,在經(jīng)濟上可行。本論文對數(shù)據(jù)挖掘中的分類技術和回歸技術進行分析,之后選取KNN算法為對二手房進行評估的核心技術,實現(xiàn)了一個B/S(Browser/Server,瀏覽器/服務器模式)估價應用,給予有二手房評估需求的目標用戶一種快速獲得評估結果的方式。經(jīng)研究分析,經(jīng)典KNN算法具有精度較高、對樣本集中的噪聲不敏感等優(yōu)點,也有k值難以選取、時間復雜度高、受樣本平衡度的影響大等缺點。本著揚長避短的思想,對于算法優(yōu)點,使用結果集加權的方式進一步提高算法精度,使用去重和標準化等方式減少噪聲;對于算法缺點,使用多次檢驗法選定k值,使用TopK算法以及多線程并發(fā)的方式降低時間復雜度,使用數(shù)據(jù)采集階段就對數(shù)據(jù)進行分類的方式穩(wěn)定樣本平衡度。為了對改進的KNN算法進行實用性驗證,利用該算法對哈爾濱市的部分二手房數(shù)據(jù)進行分析,通過對數(shù)據(jù)的預處理,基于改進KNN算法的實現(xiàn)等步驟,最終給出二手房的估價結果。對于二手房業(yè)主及中介商等目標用戶,使用改進KNN算法進行二手房評估的B/S應用對比傳統(tǒng)的二手房評估方式,具有計算速度快、交互界面友好易用的優(yōu)勢,很好的滿足了目標用戶的需求。
[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.
【學位授予單位】：哈爾濱商業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F299.23;TP311.13

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本文編號：2131706