本文選題：移動(dòng)互聯(lián)網(wǎng) + 數(shù)據(jù)挖掘��； 參考：《北京郵電大學(xué)》2012年碩士論文

【摘要】：隨著智能終端的普及和無(wú)線網(wǎng)絡(luò)的改善,移動(dòng)互聯(lián)網(wǎng)迅猛發(fā)展,移動(dòng)互聯(lián)網(wǎng)的競(jìng)爭(zhēng)也越來(lái)越激烈。當(dāng)前移動(dòng)互聯(lián)網(wǎng)存在盈利模式脆弱和用戶體驗(yàn)差等問題。電信運(yùn)營(yíng)商和服務(wù)提供商想要在競(jìng)爭(zhēng)中勝出,必須對(duì)用戶進(jìn)行細(xì)分,滿足個(gè)性化需求。 聚類分析是數(shù)據(jù)挖掘的重要組成部分,可以用來(lái)發(fā)現(xiàn)數(shù)據(jù)的分布和模式。BIRCH算法是一種經(jīng)典的聚類分析算法,它適合處理大規(guī)模的數(shù)據(jù)集。本文采用BIRCH算法實(shí)現(xiàn)用戶細(xì)分。為了提高BIRCH算法的效率,本文采用了延遲分裂機(jī)制和離群點(diǎn)處理機(jī)制。這兩種機(jī)制把異常數(shù)據(jù)保存在磁盤,統(tǒng)一處理,節(jié)約了時(shí)間。針對(duì)BIRCH算法聚類不準(zhǔn)確和重建次數(shù)過多的問題,本文提出采用動(dòng)態(tài)閾值進(jìn)行聚類的方法。其中時(shí)節(jié)點(diǎn)的閩值由離差平方和與調(diào)節(jié)因子來(lái)確定,并通過實(shí)驗(yàn)確定調(diào)節(jié)因子的最優(yōu)取值。改進(jìn)BIRCH算法在合并簇的過程中保留了簇的自然屬性,具有更好的適應(yīng)性和靈活性,經(jīng)實(shí)驗(yàn)證明改進(jìn)BIRCH算法比原始BIRCH算法的聚類效果更好。 本文在移動(dòng)應(yīng)用統(tǒng)計(jì)平臺(tái)的基礎(chǔ)上設(shè)計(jì)并開發(fā)了用戶細(xì)分工具,用戶細(xì)分工具是一個(gè)客戶機(jī)/服務(wù)器結(jié)構(gòu)。用戶細(xì)分工具采集用戶使用應(yīng)用的類型和使用應(yīng)用的時(shí)長(zhǎng)等數(shù)據(jù),利用改進(jìn)BIRCH算法對(duì)用戶進(jìn)行聚類分析,實(shí)現(xiàn)用戶興趣偏好的細(xì)分。通過用戶細(xì)分,服務(wù)提供商可以依據(jù)用戶的興趣偏好推送匹配的廣告,提高廣告的轉(zhuǎn)化率,實(shí)現(xiàn)商家與用戶的共贏。
[Abstract]:With the popularity of intelligent terminals and the improvement of wireless network, mobile Internet is developing rapidly, and the competition of mobile Internet is becoming more and more fierce. The current mobile Internet is vulnerable to profit model and poor user experience and other problems. If telecom operators and service providers want to win the competition, they must subdivide the users to meet the individual needs. Clustering analysis is an important part of data mining. It can be used to find the distribution and pattern of data. Birch algorithm is a classical clustering analysis algorithm, which is suitable for dealing with large-scale data sets. In this paper, BIRCH algorithm is used to realize user subdivision. In order to improve the efficiency of BIRCH algorithm, the delay splitting mechanism and outlier processing mechanism are adopted in this paper. These two mechanisms save exception data on disk, unified processing, save time. Aiming at the problem of inaccurate clustering and too many reconstruction times of BIRCH algorithm, a dynamic threshold clustering method is proposed in this paper. The threshold of the time node is determined by the sum of deviation square and the adjustment factor, and the optimal value of the adjustment factor is determined by experiments. The improved BIRCH algorithm retains the natural attributes of the cluster in the process of clustering, and has better adaptability and flexibility. The experimental results show that the improved BIRCH algorithm has better clustering effect than the original BIRCH algorithm. This paper designs and develops a user subdivision tool based on the mobile application statistics platform. The user subdivision tool is a client / server structure. The user subdivision tool collects the data of the type and duration of the application used by the user, and makes use of the improved BIRCH algorithm to cluster the user to realize the subdivision of the user's interest preference. Through the user segmentation, the service provider can push the matching advertisement according to the user's interest preference, improve the conversion rate of the advertisement, and realize the win-win situation between the merchant and the user.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TP311.13

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