聚类分布式传感器数据流Clustering Distributed Sensor Data Streams |
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课程网址: | http://videolectures.net/ecmlpkdd08_rodrigues_cdsd/ |
主讲教师: | Pedro Pereira Rodrigues; Joao Gama; Luis Lopes |
开课单位: | 波尔图大学 |
开课时间: | 信息不详。欢迎您在右侧留言补充。 |
课程语种: | 英语 |
中文简介: | 本文研究了传感器网络生成的数据点上连续保持簇结构的问题。我们提出了一种新的分布式算法dgclust,它通过允许每个局部传感器保持其数据流的在线离散化,从而减少了维数和通信负担。每个新的数据点触发这个单变量网格中的一个单元,反映本地站点上数据流的当前状态。每当本地站点更改其状态时,它就会通知中央服务器它所处的新状态。中心站点保存了一小部分全球最常见状态的计数器列表。将一种简单的自适应分割聚类算法应用于频繁状态中心点,给出了聚类中心的任意定义。该方法是在分布式传感器网络环境下进行评估的,为其优势提供了经验和理论证据。 |
课程简介: | In this work we study the problem of continuously maintain a cluster structure over the data points generated by a sensor network. We propose DGClust, a new distributed algorithm which reduces both the dimensionality and the communication burdens, by allowing each local sensor to keep an online discretization of its data stream. Each new data point triggers a cell in this univariate grid, reflecting the current state of the data stream at the local site. Whenever a local site changes its state, it notifies the central server about the new state it is in. The central site keeps a small list of counters of the most frequent global states. A simple adaptive partitional clustering algorithm is applied to the frequent states central points, providing an anytime definition of the clusters centers. The approach is evaluated in the context of distributed sensor networks, presenting empirical and theoretical evidence of its advantages. |
关 键 词: | 机器学习; 传感器; 数据流; 算法 |
课程来源: | 视频讲座网 |
最后编审: | 2019-12-05:cwx |
阅读次数: | 44 |