Important Dates

• Paper submission due: 11:59:59 p.m. PST, October 30 (Sun), 2016
• Notification to authors: January 13 (Fri), 2017
• Camera-ready due: February 13 (Mon), 2017
• Conference: May 23 (Tues) - 26 (Fri), 2017

Conference Scope

The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is a leading international conference in the areas of knowledge discovery and data mining (KDD). It provides an international 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.

Topics

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

• Theoretic foundations
• Novel models and algorithms
• Association analysis
• Clustering
• Classification
• Statistical methods for data mining
• 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 multimedia data
• Mining scientific data
• Privacy preserving data mining
• Anomaly detection
• 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 etc.

Paper Submission

The submitted paper should adhere to the double-blind review policy. All papers will be double-blind reviewed by the Program Committee on the basis of technical quality, relevance to data mining, originality, significance, and clarity. All paper submissions will be handled electronically. Detailed instructions are provided on the conference home page. Papers that do not comply with the Submission Guidelines will be rejected without review.

Each submitted paper should include an abstract up to 200 words and be no longer than 12 single-spaced pages with 10pt font size. Authors are strongly encouraged to use Springer LNCS/LNAI manuscript submission guidelines for their initial submissions. All papers must be submitted electronically through the paper submission system in PDF format only.

The submitted papers must not be previously published anywhere and must not be under consideration by any other conference or journal during the PAKDD review process. Submitting a paper to the conference means that if the paper was accepted, at least one author will attend the conference to present the paper. For no-show authors, their papers will not be included in the proceedings.

The conference will confer several awards including Best Paper Awards, Best Student Paper Awards, and Best Application Paper Awards from the submissions.

The proceedings of the conference will be published by Springer as a volume of the LNAI series, and selected excellent 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.

Before submitting your paper, please carefully read and agree with the PAKDD submission policy and no-show policy: http://pakdd.org/policy.html.

Further Information

For further information, please contact the Program Committee Chairs at pakdd2017 (at) gmail.com.