Conference Information
DMKD 2020: International Conference on Data Mining and Knowledge Discovery
http://www.icdmkd.org/
Submission Date:
2019-11-23
Notification Date:
2019-11-28
Conference Date:
2020-02-17
Location:
Sydney, Australia
Years:
3
Viewed: 6122   Tracked: 18   Attend: 1

Conference Location
Advertisment
Call For Papers
★2020年第三届数据挖掘与知识发现国际会议(DMKD 2020) ---Ei Compendex&Scopus-Call for papers
|2020年2月17-19日,澳大利亚悉尼|网站: www.icdmkd.org 

★DMKD 2020将围绕“数据挖掘与知识发现”的最新研究领域而展开,为研究人员,工程师和学者,以及行业专业人士提供一个平台并介绍他们的最新的研究成果以及今后开发的活动,为参会人员们交流新的思想和应用经验建立业务或研究关系。本次会议将于2020年2月17-19日在澳大利亚悉尼召开,在会议期间您将有机会聆听到前沿的学术报告,见证该领域的成果与进步。

★出版和索引
所有接受的论文将在线出版,将被Ei Compendex,SCOPUS,Google Scholar,Cambridge Scientific Abstracts(CSA),Inspec, ISTP等检索,优秀论文将在国际期刊上发表。热忱地欢迎从事相关技术研究的各专业技术人员踊跃投稿并参加大会。

★节目预览/节目一览
2月17日:注册+接待
2月18日:开幕式+ KN演讲+分会场报告
2月19日:分会场报告+实验室参观

★论文提交及要求
1. 通过CMT提交PDF版本:https://cmt3.research.microsoft.com/DMKD2020
2.投稿邮箱:dmkd@iased.org   
3.投稿要求:
(1)稿件必须为全英文稿件,图片、表格、公式中均不允许有中文出现,稿件需与主题相关。
(2)请作者按照官网上模板的格式编排(最好是在模板的基础上替换原文内容)。本次会议采取先投稿、先送审、符合条件者先发送录用通知方式进行。审稿周期约为3-7个工作日。
(3)投稿的文章经过初审之后,将提交给至少2名审稿委员会成员进行同行审查,审查内容包括原创性、技术或研究的内容和深度、会议主题相关性、贡献性和可读性。
(4)如果只参会做报告,不出版,则只需提交摘要以供审阅。听众不需要提交稿件,注册成功的听众可以参加会议的所有分会。
(5)稿件不允许有剽窃行为,涉嫌抄袭的文章将不会送审。

★联系我们
邓女士
电子邮件:dmkd@iased.org
网站:www.icdmkd.org 
QQ: 2011307354(加QQ时请附上您想咨询的会议名字简称,如:DMKD 2020)

The topics of relevance for the conference papers include but not limited to the following:

    Anomaly detection
    Applications to healthcare, bioinformatics, computational chemistry, finance, eco-informatics, marketing, gaming, cyber-security etc.
    Association analysis
    Classification
    Clustering
    Data pre-processing
    Feature extraction and selection
    Fraud and risk analysis
    Human, domain, organizational and social factors in data mining
    Integration of data warehousing, OLAP and data mining
    Interactive and online mining
    Mining behavioral data
    Mining dynamic/streaming data
    Mining graph and network data
    Mining heterogeneous/multi-source data
    Mining high dimensional data
    Mining imbalanced data
    Mining multimedia data
    Mining scientific data
    Mining sequential data
    Mining social networks
    Mining spatial and temporal data
    Mining uncertain data
    Mining unstructured and semi-structured data
    Novel models and algorithms
    Opinion mining and sentiment analysis
    Parallel, distributed, and cloud-based high performance data mining
    Post-processing including quality assessment and validation
    Privacy preserving data mining
    Security and intrusion detection
    Statistical methods for data mining
    Theoretic foundations
    Ubiquitous knowledge discovery and agent-based data mining
    Visual data mining
Last updated by Zofia Zeng in 2019-08-25
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