图 书 馆
流数据挖掘：大数据视角Stream Data Mining: A Big Data Perspective |
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| 课程网址: | https://videolectures.net/videos/kdd2016_khan_data_perspective |
| 主讲教师: | Latifur Khan |
| 开课单位: | KDD 2016研讨会 |
| 开课时间: | 2025-02-04 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Data streams are continuous flows of data. Examples of data streams include network traffic, sensor data, call center records and so on. Data streams demonstrate several unique properties that together conform to the characteristics of big data (i.e., volume, velocity, variety and veracity) and add challenges to data stream mining. In this talk we will present an organized picture on how to handle various data mining techniques in data streams. Most existing data stream classification techniques ignore one important aspect of stream data: arrival of a novel class. We address this issue and propose a data stream classification technique that integrates a novel class detection mechanism into traditional classifiers, enabling automatic detection of novel classes before the true labels of the novel class instances arrive. Novel class detection problem becomes more challenging in the presence of concept-drift, when the underlying data distributions evolve in streams. In this talk we will show how to make fast and correct classification decisions under this constraint with limited labeled training data and apply them to real benchmark data. In addition, we will present a number of stream classification applications such as adaptive malicious code detection, website fingerprinting, evolving insider threat detection and textual stream classification. This research was funded in part by NSF, NASA, Air Force Office of Scientific Research (AFOSR) and Raytheon. |
| 关 键 词: | 流数据挖掘; 大数据; 标记训练数据 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2025-04-06：liyq |
| 最后编审: | 2025-04-06：liyq |
| 阅读次数: | 8 |