机译:Top-κ封闭的共同发生模式采用多个流的差异隐私
Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal University Guilin China School of Computer Science and Information Engineering Guangxi Normal University Guilin China;
School of Computer Science and Information Engineering Guangxi Normal University Guilin China;
School of Computer Science and Information Engineering Guangxi Normal University Guilin China;
School of Computer Science and Information Engineering Guangxi Normal University Guilin China;
Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal University Guilin China School of Computer Science and Information Engineering Guangxi Normal University Guilin China;
Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal University Guilin China School of Computer Science and Information Engineering Guangxi Normal University Guilin China;
Differential privacy; Multiple streams; Co-occurrence patterns; Data mining; Sliding window;
机译:跨多个流挖掘Top-k共现模式
机译:跨多个数据流的分布式频繁共现模式算法研究
机译:共振 - 通过在多路复用轨迹中采矿共同发生模式的社会连接推动的智能分析框架
机译:挖掘多个流的顶级K共发生模式
机译:使用SSM算法在数据流中挖掘频繁的顺序模式。
机译:多个应激源在流sal中产生不同的转录组模式
机译:使用模式共现矩阵来清理封闭的顺序模式以进行文本挖掘