Mining Data Streamsdoi:10.1017/CBO9781139924801.005Leskovec, JureRajaraman, AnandUllman, Jeffrey David
...MiningComplex Knowledge from Complex Data(复杂数据&复杂知识) 图形类复杂知识。如何从大数据中发现图形和结构化模式的主题。...DataMiningin a Network Setting(网络挖掘) 5.1...Miningin and for computer networks — high-speedminingof high-speed streams 计算机(通信)网络挖掘问题。...Distributed DataMin...
choosing various building and validation models, and the Deployment stage(generating expected outcomes). Conversely, it is not as simple to work as it is essential to understand what and how it can be implemented in all the data streams with massive data production around the organisations...
Data Mining Clustering Technique in Data Streams – A Survey A data stream is a continuous, real time, ordered sequence of items. It is impossible to control the order in which items arrives. Real time surveillances ... S Vijayarani,P Sathya - 《Data Mining & Knowledge Engineering》 被引...
[43] J. Shafer, R. Agrawal, and M. Mehta, "SPRINT: A Scalable Parallel Classifier for Data Mining," Proc. 22nd VLDB Conf., 1996. [44] A. da Silva, R. Chiky, and G. He´brail, "A Clustering Approach for Sampling Data Streams in Sensor Networks," Knowledge and Information Systems...
This implies that the algorithm clusters the clickstreams based on the number of actions performed by the user during the given session. We can represent the clusters graphically as below: In the above graph, the Y-axis denotes a unique identifier for each session. The X-axis corresponds to ...
·实时数据流(Real-time Data Streams):处理和分析实时数据流以支持即时决策,例如,实时监控和动态调整。 ·实时决策支持(Real-time Decision Support):提供实时决策支持和反馈,例如,实时预测和调整策略。 ·数据驱动的智能系统(Data-driven Intelligent Systems):构建基于数据驱动的智能系统以提高决策效率,例如,智能控制...
Most of the algorithms described in this book assume that we are mining a database. That is, all ourdata is availablewhen and if we want it. In this chapter, we shall make another assumption:data arrives in a stream or streams, and if it is not processed immediately or stored, then ...
data streams. In other words, the running time of a data mining algorithm must be predictable, short, and acceptable by applications.Efficiency, scalability, performance, optimization, and the ability to execute inreal timeare key criteria that drive the development of many new data mining ...
Data mining in big data The use of data mining rose significantly over the past twenty years as more data sources provided a big data environment. Big data refers to massive volumes of data, often in continuous streams from multiple sources and at high velocity. In the early days of ...