發(fā)布時(shí)間：2018-01-08 10:04

  本文關(guān)鍵詞：大數(shù)據(jù)環(huán)境下企業(yè)銷(xiāo)售數(shù)據(jù)處理方法與市場(chǎng)感知研究　出處：《浙江理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： KNN算法 市場(chǎng)感知 ARIMA預(yù)測(cè)模型 灰色預(yù)測(cè)模型

【摘要】：隨著企業(yè)信息化的推進(jìn)與發(fā)展,銷(xiāo)售數(shù)據(jù)急聚增加,由于銷(xiāo)售數(shù)據(jù)在企業(yè)決策中的重要作用,挖掘銷(xiāo)售數(shù)據(jù)中的有用信息是亟待公司解決的問(wèn)題。研究出能夠在大數(shù)據(jù)環(huán)境下挖掘銷(xiāo)售數(shù)據(jù)有效信息的數(shù)據(jù)處理方法,正確地使用海量數(shù)據(jù)中挖掘出來(lái)的有效信息也是企業(yè)的迫切需求。本文利用海量銷(xiāo)售數(shù)據(jù)中包含的銷(xiāo)售數(shù)據(jù)走勢(shì)預(yù)測(cè)未來(lái)銷(xiāo)售數(shù)據(jù)的趨勢(shì),感知市場(chǎng)狀況,掌握市場(chǎng)動(dòng)向,給企業(yè)銷(xiāo)售決策者提供有效的銷(xiāo)售數(shù)據(jù)動(dòng)向參考信息,為生產(chǎn)、營(yíng)銷(xiāo),以及判斷市場(chǎng)狀況提供決策依據(jù)。圍繞以上問(wèn)題,本文對(duì)于大數(shù)據(jù)環(huán)境下的企業(yè)銷(xiāo)售數(shù)據(jù)挖掘算法和企業(yè)市場(chǎng)的預(yù)測(cè)模型做了一下主要研究:(1)運(yùn)用Hadoop平臺(tái)存儲(chǔ)大數(shù)據(jù),并且運(yùn)用Hadoop的MapReduce抽取需要處理的數(shù)據(jù),并導(dǎo)入到關(guān)系型數(shù)據(jù)庫(kù)中,根據(jù)數(shù)據(jù)挖掘算法中對(duì)數(shù)據(jù)結(jié)構(gòu)的需求,針對(duì)數(shù)據(jù)中的不同的數(shù)據(jù)異常對(duì)數(shù)據(jù)使用不同的清洗策略進(jìn)行清洗與數(shù)據(jù)規(guī)范,再將處理后的數(shù)據(jù)交付給關(guān)系型數(shù)據(jù)庫(kù)。(2)針對(duì)傳統(tǒng)的大數(shù)據(jù)挖掘算法存在的問(wèn)題,本文提出了基于分塊后重疊k-means聚類的KNN分類算法,算法通過(guò)給傳統(tǒng)KNN算法增加一個(gè)訓(xùn)練過(guò)程的方式讓KNN算法能夠運(yùn)用于大數(shù)據(jù)環(huán)境,并且能夠快速準(zhǔn)確地對(duì)數(shù)據(jù)進(jìn)行分類,大大提升了分類算法的效率。并且通過(guò)新算法,對(duì)零售戶數(shù)據(jù)中的幾個(gè)規(guī)格卷煙的銷(xiāo)售詳情進(jìn)行分類,統(tǒng)計(jì)其分類結(jié)果,與實(shí)際的數(shù)據(jù)進(jìn)行了對(duì)比,驗(yàn)證了算法的可行性與準(zhǔn)確性。(3)分析各類預(yù)測(cè)模型對(duì)于本文的研究?jī)?nèi)容的適用性,根據(jù)本文的數(shù)據(jù)特點(diǎn)以及預(yù)期的預(yù)測(cè)結(jié)果數(shù)據(jù)特點(diǎn)選擇了適合的預(yù)測(cè)模型:差分自回歸滑動(dòng)平均模型(ARIMA(p,d,q))與灰色模型,作為本文的市場(chǎng)感知模型的基礎(chǔ)。(4)以企業(yè)的零售數(shù)據(jù)為實(shí)驗(yàn)數(shù)據(jù),建立結(jié)合ARIMA差分自回歸滑動(dòng)平均模型與灰色模型的市場(chǎng)感知模型。根據(jù)ARIMA自回歸移動(dòng)平均模型能夠準(zhǔn)確地預(yù)測(cè)未來(lái)短期的銷(xiāo)售數(shù)據(jù),但是,由于隨著預(yù)測(cè)時(shí)間越長(zhǎng)預(yù)測(cè)的準(zhǔn)確率越低的特點(diǎn),在ARIMA模型的基礎(chǔ)上使用灰色拓?fù)淠Ｐ瓦M(jìn)行長(zhǎng)期的銷(xiāo)售數(shù)據(jù)預(yù)測(cè),讓企業(yè)能夠看到的不僅僅是未來(lái)半年或者一年內(nèi)的銷(xiāo)售數(shù)據(jù)的預(yù)測(cè),而且能給企業(yè)提供更加準(zhǔn)確掌握未來(lái)市場(chǎng)動(dòng)向的數(shù)據(jù)。
[Abstract]:With the promotion and development of enterprise information, sales data is increasing rapidly, because of the important role of sales data in enterprise decision-making. Mining useful information in sales data is an urgent problem to be solved by the company. A data processing method which can mine effective information of sales data in big data environment is developed. It is also an urgent need for enterprises to use the valid information extracted from mass data correctly. This paper uses the trend of sales data contained in mass sales data to predict the trend of future sales data and to perceive the market situation. To grasp the market trends, to provide effective sales data to the decision-makers of the enterprise sales trends reference information, for production, marketing, and to judge the market situation decision-making basis. Around the above issues. In this paper, the enterprise sales data mining algorithm and the enterprise market prediction model under the big data environment are mainly studied. (1) the Hadoop platform is used to store big data. And use the MapReduce of Hadoop to extract the data to be processed, and import into the relational database, according to the data mining algorithm of the data structure requirements. According to the different data anomalies in the data, different cleaning strategies are used to clean and standardize the data. Then the processed data is delivered to the relational database. 2) aiming at the problems of the traditional big data mining algorithm, this paper proposes a KNN classification algorithm based on overlapping k-means clustering. By adding a training process to the traditional KNN algorithm, the algorithm enables the KNN algorithm to be applied to big data environment, and can quickly and accurately classify the data. Greatly improve the efficiency of the classification algorithm. And through the new algorithm, the retail data of several specifications of cigarette sales details classification, statistics of the classification results, and compared with the actual data. The feasibility and accuracy of the algorithm are verified. According to the characteristics of the data in this paper and the characteristics of the predicted results, a suitable prediction model, namely, the differential autoregressive moving average model (ARIMAPA) and the grey model, is selected. As the basis of the market perception model of this paper, we take the retail data of enterprises as the experimental data. A market perception model combining ARIMA differential autoregressive moving average model and grey model is established. According to the ARIMA autoregressive moving average model, the future short-term sales data can be accurately predicted, but. Because the prediction accuracy is lower with the longer the forecast time, the grey topology model is used to predict the long-term sales data on the basis of ARIMA model. What allows the enterprise to see is not only the forecast of the sales data in the next six months or a year, but also provides the enterprise with the data which can grasp the future market movement more accurately.
【學(xué)位授予單位】：浙江理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F274;TP311.13

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