Acm Transactions On Knowledge Discovery From Data SCISCIE

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.

TKDD 欢迎有关知识发现和分析各种形式数据的全方位研究的论文。此类主题包括但不限于：用于数据挖掘和大数据分析的可扩展和有效算法、挖掘大脑网络、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，挖掘空间和时间数据，用于社区生成的数据挖掘，社交网络分析和图形结构化数据，数据挖掘中的安全和隐私问题，可视化、交互式和在线数据挖掘，数据挖掘的预处理和后处理、稳健和可扩展的统计方法、数据挖掘语言、数据挖掘基础、KDD 框架和过程，以及利用数据挖掘技术的新型应用程序和基础设施，包括大规模并行处理和云计算平台。 TKDD 鼓励在大型分布式计算机网络、并行或多处理计算机或新数据设备的背景下探索上述主题的论文。 TKDD 还鼓励发表描述当前数据挖掘技术无法满足的新兴数据挖掘应用的论文。

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