【摘要】：現(xiàn)在大量信息已成為社會的主要特征,然而如何在海量數(shù)據(jù)中發(fā)現(xiàn)有用的信息是人們面對的一個重要課題。數(shù)據(jù)挖掘就是在大型數(shù)據(jù)庫中自動發(fā)現(xiàn)有用信息的過程,數(shù)據(jù)挖掘中關(guān)聯(lián)分析的Apriori算法最早被應(yīng)用于超市營銷中,隨后超市的大量信息資源一直伴隨著數(shù)據(jù)挖掘技術(shù)的發(fā)展而不斷使用。但是我國超市零售業(yè)并沒有對顧客的大量信息開展有效的研究,也不能很好地利用數(shù)據(jù)挖掘方法對超市的決策提供有價值的幫助。本文收集到某家超市的實(shí)際交易數(shù)據(jù),并針對關(guān)聯(lián)分析在超市營銷決策中的應(yīng)用展開研究。 本文首先介紹了數(shù)據(jù)挖掘的相關(guān)理論、SQL的基礎(chǔ)知識、IBM SPSS Modeler關(guān)聯(lián)分析的理論,然后在理論研究的基礎(chǔ)上應(yīng)用SQL對數(shù)據(jù)進(jìn)行預(yù)處理和基本的描述分析,來了解該超市總體的銷售情況。其次利用預(yù)處理得到的數(shù)據(jù)集,應(yīng)用專業(yè)的數(shù)據(jù)挖掘軟件IBM SPSS Modeler,對超市中的商品購買進(jìn)行關(guān)聯(lián)分析。 在商品購買的關(guān)聯(lián)分析中,本文首先研究商品品類之間的關(guān)聯(lián)規(guī)則分析,并且利用各類商品交叉銷售所產(chǎn)生的利潤來評價關(guān)聯(lián)規(guī)則價值,解決了傳統(tǒng)的支持度和置信度可能在統(tǒng)計(jì)學(xué)上得出非常令人滿意的結(jié)論,卻忽略了企業(yè)更加注重商品利潤這一問題,進(jìn)而來研究各類商品的貨架擺放問題。然后再對各商品進(jìn)行關(guān)聯(lián)分析,進(jìn)行了優(yōu)化商品布局、設(shè)計(jì)促銷方案和快速商品推薦業(yè)務(wù)的應(yīng)用。本文提供的方法具有一定的合理性和優(yōu)越性,在實(shí)際的超市營銷中具有一定的推廣價值。
[Abstract]:Nowadays, a large amount of information has become the main feature of the society. However, how to find useful information in the massive data is an important issue that people face. Data mining is the process of discovering useful information automatically in large databases. The Apriori algorithm of association analysis in data mining is first applied in supermarket marketing. Subsequently, a large number of information resources in supermarkets have been continuously used with the development of data mining technology. However, the retail business of supermarkets in China has not carried out effective research on a large amount of customer information, nor has it been able to make good use of data mining methods to provide valuable help for supermarket decision-making. In this paper, the actual transaction data of a supermarket are collected, and the application of association analysis in supermarket marketing decision is studied. This paper first introduces the related theories of data mining, the basic knowledge of SQL, the theory of, IBM SPSS Modeler association analysis, and then uses SQL to preprocess and describe the data on the basis of theoretical research. To understand the overall sales of the supermarket. Secondly, using the data set obtained by preprocessing, using the professional data mining software IBM SPSS Modeler, to analyze the commodity purchase in supermarket. In the correlation analysis of commodity purchase, this paper first studies the analysis of association rules among commodity categories, and evaluates the value of association rules by using the profits generated by cross-selling of all kinds of commodities. It solves the problem that the traditional support and confidence may reach a very satisfactory conclusion in statistics, but ignores the problem that enterprises pay more attention to the profit of goods, and then studies the shelf placement of all kinds of goods. Then we analyze the relationship of each commodity, optimize the product layout, design the promotion scheme and the application of fast commodity recommendation business. The method provided in this paper has certain rationality and superiority, and has certain promotion value in the actual supermarket marketing.
【學(xué)位授予單位】：云南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TP311.13

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本文編號：2313204