Conference Information
PAKDD 2018 : Pacific-Asia Conference on Knowledge Discovery and Data Mining
http://prada-research.net/pakdd18/
Submission Date:
2017-11-21 Extended
Notification Date:
2018-01-28
Conference Date:
2018-05-15
Location:
Melbourne, Australia
Years:
22
CCF: c   CORE: a   Viewed: 25598   Tracked: 94   Attend: 18

Conference Location
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Call For Papers
Conference Scope

The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) provides an internationally prestigious forum for researchers and industry practitioners to share their new ideas, original research results and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems and the emerging applications. The conference will also feature a high-quality and timely series of tutorials, and a wide range of workshops. Typically, the Program Committee will also select Best Paper Award, Best Student Paper Award and Best Application Award. The proceedings of the conference will be published by Springer as a volume of the LNAI series and a small number of selected papers will be invited for publications in special issues of high-quality journals including Knowledge and Information Systems (KAIS) and International Journal of Data Science and Analytics.

Topics

As a premier international conference on knowledge discovery and data mining, PAKDD’18 welcomes all submissions on all aspects of knowledge discovery, data mining and machine learning. Suggestive topics of relevance for the conference include, but not limited to, the following:

    Theoretic foundations of KDD
    Deep learning theory and applications in KDD
    Novel models and algorithms
    Statistical methods and graphical models for data mining
    Anomaly detection and analytics
    Association analysis
    Clustering
    Classification
    Data pre-processing
    Feature extraction and selection
    Post-processing including quality assessment and validation
    Mining heterogeneous/multi-source data
    Mining sequential data
    Mining spatial and temporal data
    Mining unstructured and semi-structured data
    Mining graph and network data
    Mining social networks
    Mining high dimensional data
    Mining uncertain data
    Mining imbalanced data
    Mining dynamic/streaming data
    Mining behavioral data
    Mining multi-media data
    Mining scientific data
    Privacy preserving data mining
    Fraud and risk analysis
    Security and intrusion detection
    Visual data mining
    Interactive and online mining
    Ubiquitous knowledge discovery and agent-based data mining
    Integration of data warehousing, OLAP, and data mining
    Parallel, distributed, and cloud-based high-performance data mining
    Opinion mining and sentiment analysis
    Human, domain, organizational, and social factors in data mining
    Applications to healthcare, bioinformatics, computational chemistry, finance, eco-informatics, marketing, gaming, cyber-security, and industry-related problems
Last updated by Dou Sun in 2017-11-11
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