本文關(guān)鍵詞：蟻群與K均值聚類算法融合研究及其在用戶分群中的應(yīng)用　出處：《西南科技大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 數(shù)據(jù)挖掘 聚類 K-means 聚類 蟻群聚類 用戶分群

【摘要】：如今市場競爭日益激烈,企業(yè)面對數(shù)量巨大的消費(fèi)者群體,傳統(tǒng)的大眾化營銷的成本高且沒有優(yōu)勢。對用戶群體進(jìn)行細(xì)分,針對不同用戶群體進(jìn)行精準(zhǔn)的市場營銷,能提高企業(yè)營銷策略的效率,獲得更好的營銷效果。傳統(tǒng)數(shù)據(jù)分析方法難以處理海量的用戶數(shù)據(jù),因此,采用數(shù)據(jù)挖掘技術(shù)中的聚類分析算法實(shí)現(xiàn)企業(yè)用戶分群。論文給出了營銷原理及數(shù)據(jù)挖掘技術(shù)相關(guān)理論說明,研究了聚類分析技術(shù)中K-means聚類算法和蟻群聚類算法的基本原理。分析了長虹社區(qū)論壇的用戶數(shù)據(jù),根據(jù)用戶數(shù)據(jù)數(shù)量大、屬性多的特點(diǎn),提出適用于論壇用戶聚類的改進(jìn)算法Ant-K-means聚類算法。研究了K-means聚類算法對簇?cái)?shù)量的選取和初始聚類中心的質(zhì)量敏感以及蟻群聚類算法搜索時(shí)間長的問題解決方法。完成了蟻群聚類算法對數(shù)據(jù)集中抽取的部分樣品數(shù)據(jù)的聚類,并將蟻群聚類獲取的簇?cái)?shù)量和簇中心作為輸入?yún)?shù),使用本文改進(jìn)的基于信息素的K-means算法對全部數(shù)據(jù)進(jìn)行快速聚類,獲得最終聚類結(jié)果。將改進(jìn)后的聚類算法應(yīng)用到長虹智能電視的論壇用戶分群中,根據(jù)用戶的行為數(shù)據(jù)將用戶聚類到不同的簇,對各簇的用戶進(jìn)行了分析與可視化。將改進(jìn)算法應(yīng)用到長虹論壇的用戶分群中,實(shí)驗(yàn)結(jié)果顯示分群效果良好,群體內(nèi)用戶特征相似性較高,驗(yàn)證了改進(jìn)算法的正確性和有效性,為長虹智能電視的產(chǎn)品營銷與用戶服務(wù)提供了決策依據(jù)。
[Abstract]:Nowadays, the market competition is increasingly fierce, enterprises face a large number of consumer groups, traditional popular marketing has high cost and no advantages. Accurate marketing for different user groups can improve the efficiency of enterprise marketing strategies and achieve better marketing results. Traditional data analysis method is difficult to deal with massive user data, so. The clustering analysis algorithm of data mining technology is used to realize the clustering of enterprise users. The marketing principle and the related theory of data mining technology are given in this paper. This paper studies the basic principles of K-means clustering algorithm and ant colony clustering algorithm in clustering analysis technology, and analyzes the user data of Changhong Community Forum, according to the characteristics of large number of user data and many attributes. An improved Ant-K-means clustering algorithm for forum users is proposed. The sensitivity of K-means clustering algorithm to the selection of cluster numbers and the quality of initial clustering centers and ant colony clustering are studied. To solve the problem of long searching time, the ant colony clustering algorithm is used to cluster part of the sample data extracted from the data set. The number of clusters and cluster centers obtained by ant colony clustering are taken as input parameters, and the improved K-means algorithm based on pheromone is used to cluster all the data quickly. The improved clustering algorithm is applied to the forum user cluster of Changhong Intelligent TV, and the users are clustered into different clusters according to the user behavior data. The improved algorithm is applied to the user cluster of Changhong Forum. The experimental results show that the clustering effect is good and the similarity of user characteristics within the group is high. The correctness and effectiveness of the improved algorithm are verified, and the decision basis for the product marketing and customer service of Changhong Intelligent TV is provided.
【學(xué)位授予單位】：西南科技大學(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：1367584