会议名称(中文): 第20届ACM SIGKDD知识发现与数据挖掘会议 会议名称(英文): 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 所属学科: 计算机科学理论,计算机软件,计算机应用技术 开始日期: 2014-08-24 结束日期: 2014-08-27 所在国家: 美国 所在城市: 美国 具体地点: New York 主办单位: 美国计算机学会
[ 重要日期 ] 摘要截稿日期: 2014-02-13 全文截稿日期: 2014-02-21
[ 会务组联系方式 ] E-MAIL: gc@kdd2014.org 会议网站: http://www.kdd.org/kdd2014/ 会议背景介绍: Welcome to KDD 2014, an interdisciplinary conference that brings together researchers and practitioners from all aspects of data mining, knowledge discovery, and large-scale data analytics.
This year, we have a special theme: Data Mining for Social Good. It will highlight how the work of data analytics researchers and practitioners contributes towards social good, and how these high impact social problems provide a rich set of challenges for KDD researchers to work on. 征文范围及要求: Papers submitted to the Research track are solicited in all areas of data mining, knowledge discovery, and large-scale data analytics, including, but not limited to:
Algorithms: Graph and link mining, rule and pattern mining, web mining, dimensionality reduction and manifold learning, combinatorial optimization, relational and structured learning, matrix and tensor methods, classification and regression methods, semi-supervised learning, and unsupervised learning and clustering. Applications: Innovative applications that use data mining, including systems for social network analysis, recommender systems, mining sequences, time series analysis, online advertising, bioinformatics, systems biology, text/web analysis, mining temporal and spatial data, and multimedia processing. Big Data: Efficient and distributed data mining platforms and algorithms, systems for large-scale data analytics of textual and graph data, large-scale machine learning systems, distributed computing (cloud, map-reduce, MPI), large-scale optimization, and novel statistical techniques for big data. Data mining for social good: Novel algorithms and applications of data mining to societal problems is especially encouraged. (For deployment of existing algorithms consider the Industry/Govt. track.) Topics include: public policy, sustainability, climate change, medicine and health, education, transportation, biodiversity and energy. Foundations of data mining: Data mining methodology, data mining model selection, visualization, asymptotic analysis, information theory, and security and privacy.
|