ACM Transactions on Knowledge Discovery from Data

期刊简介：TKDD welcomes papers on a full range of research in the knowledge discovery and analysis of diverse forms of data. Such subjects include, but are not limited to: scalable and effective algorithms for data mining and big data analysis, mining brain networks, mining data streams, mining multi-media data, mining high-dimensional data, mining text, Web, and semi-structured data, mining spatial and temporal data, data mining for community generation, social network analysis, and graph structured data, security and privacy issues in data mining, visual, interactive and online data mining, pre-processing and post-processing for data mining, robust and scalable statistical methods, data mining languages, foundations of data mining, KDD framework and process, and novel applications and infrastructures exploiting data mining technology including massively parallel processing and cloud computing platforms. TKDD encourages papers that explore the above subjects in the context of large distributed networks of computers, parallel or multiprocessing computers, or new data devices. TKDD also encourages papers that describe emerging data mining applications that cannot be satisfied by the current data mining technology.

【译文】TKDD欢迎关于知识发现和分析各种数据形式的全面研究论文。这些主题包括但不限于：数据挖掘和大数据分析的可扩展和有效算法、脑网络挖掘、数据流挖掘、多媒体数据挖掘、高维数据挖掘、文本、Web和半结构化数据挖掘、时空数据挖掘、社区生成数据挖掘、社会网络分析和图结构数据、数据挖掘中的安全和隐私问题、可视、交互和在线数据挖掘、数据挖掘的前处理和后处理、鲁棒和可扩展的统计方法、数据挖掘语言、数据挖掘的基础、KDD框架和流程，以及利用数据挖掘技术（包括大规模并行处理和云计算平台）的新型应用和基础设施。TKDD鼓励在大型分布式计算机网络、并行或多处理器计算机或新型数据设备的环境中探索上述主题的论文。TKDD还鼓励描述新兴数据挖掘应用，这些应用不能由当前数据挖掘技术满足的论文。