移动应用统计平台用户细分工具的设计与实现

发布时间：2018-05-04 12:05

本文选题：移动互联网 + 数据挖掘　；参考：《北京邮电大学》2012年硕士论文

【摘要】：随着智能终端的普及和无线网络的改善,移动互联网迅猛发展,移动互联网的竞争也越来越激烈。当前移动互联网存在盈利模式脆弱和用户体验差等问题。电信运营商和服务提供商想要在竞争中胜出,必须对用户进行细分,满足个性化需求。聚类分析是数据挖掘的重要组成部分,可以用来发现数据的分布和模式。BIRCH算法是一种经典的聚类分析算法,它适合处理大规模的数据集。本文采用BIRCH算法实现用户细分。为了提高BIRCH算法的效率,本文采用了延迟分裂机制和离群点处理机制。这两种机制把异常数据保存在磁盘,统一处理,节约了时间。针对BIRCH算法聚类不准确和重建次数过多的问题,本文提出采用动态阈值进行聚类的方法。其中时节点的闽值由离差平方和与调节因子来确定,并通过实验确定调节因子的最优取值。改进BIRCH算法在合并簇的过程中保留了簇的自然属性,具有更好的适应性和灵活性,经实验证明改进BIRCH算法比原始BIRCH算法的聚类效果更好。本文在移动应用统计平台的基础上设计并开发了用户细分工具,用户细分工具是一个客户机/服务器结构。用户细分工具采集用户使用应用的类型和使用应用的时长等数据,利用改进BIRCH算法对用户进行聚类分析,实现用户兴趣偏好的细分。通过用户细分,服务提供商可以依据用户的兴趣偏好推送匹配的广告,提高广告的转化率,实现商家与用户的共赢。
[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.
【学位授予单位】：北京邮电大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TP311.13

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