Home \ Call for Papers

Important Dates

  • Abstract Submission Due: 25 September, 2011 (Sunday)
  • Paper Submission Due: 2 October, 2011 (Sunday)
  • Author Notification: 30 December, 2011 (Friday)
  • Camera Ready Due: 22 January, 2012 (Sunday)
  • Workshop Proposal Due: 28 August, 2011 (Sunday)
  • Workshop Notification: 11 September, 2011 (Sunday)
  • Tutorial Proposal Due: 13 November, 2011 (Sunday)
  • Tutorial Notification: 4 December, 2011 (Sunday)
  • Conference: 29 May - 1 June, 2012

Conference Scope

The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is a leading international conference in the areas of data mining and knowledge discovery (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, aritificial intelligence, databases, statistics, knowledge engineering, visualization, and decision-making systems.

The conference calls for research papers reporting original investigation results and industrial papers reporting real data mining applications and system development experience.

The conference will confer a Best Paper Award to the best full paper, and the Best Student Papers from amongst the student submissions. The proceedings of the conference will be published by Springer as a volume of the LNAI series.

PAKDD2012 will be held in Kuala Lumpur, one of the most attractive cities in Malaysia.


The topics of the conference and workshop papers fall into three major categories that will include but are not limited to the following:

A. Data Mining Foundations

  • Theoretic foundations
  • Novel models and algorithms
  • Mining emerging data types
  • Mining mixed and multi-source data
  • Mining complex sequential data
  • Mining spatial and temporal data
  • Mining textual and semi-structured/unstructured data
  • Parallel, distributed and combined data mining
  • Privacy data analysis
  • Mining high dimensional data
  • Statistical foundations

B. Mining in Emerging Domains

  • Stream/dynamic data mining
  • Visual data mining
  • Mining behavioral data
  • Ubiquitous knowledge discovery
  • Mining multi-agent data and agent-based data mining
  • Mining linkages, networks and communities
  • Mining the Internet and social networks
  • Financial data mining
  • Opinion and sentiment analysis
  • Mining imbalanced data
  • Mining graphic data
  • Security, risk, cost, impact, trust and repeatibility etc.
  • Interactive and online mining
  • Integration of data warehousing, OLAP and data mining
  • Massive data mining on cloud platforms

C. Process and Applications

  • Actionable knowledge discovery
  • Developing a unifying theory of data mining
  • Data pre-processing and transformation
  • Feature selection and extraction
  • Post-processing and post mining
  • Deliverable representation and presentation
  • Automating the mining process
  • Human, domain, organizational and social factors in data mining
  • Quality assessment and validation
  • Data mining languages
  • High performance implementations of data mining algorithms
  • Intrusion detection and surveillance analysis
  • Healthcare, health, drug and medical data analysis
  • Bioinformatics, computational chemistry, ecoinformatics
  • Fraud and risk analysis
  • Other applications such as supply chain intelligence
  • Lessons and experiences

organizing committee

Conference Co-Chairs

  • Philip Yu (University of Illinois at Chicago, USA)
  • Hong-Tat Ewe (Universiti Tunku Abdul Rahman, Malaysia)
  • Ee-Peng Lim (Singapore Management University, Singapore)

Program Co-Chairs

  • Pang-Ning Tan (Michigan State University, USA)
  • Sanjay Chawla (The University of Sydney, Australia)
  • Chin-Kuan Ho (Multimedia University, Malaysia)

Workshop Co-Chairs

  • Takashi Washio (Osaka University, Japan)
  • Jun Luo (Shenzhen Institute of Advanced Technology, China)

Local Organization Chairs

  • Victor Tan (Universiti Tunku Abdul Rahman, Malaysia)
  • Wen-Cheong Chin (Multimedia University, Malaysia)
  • Soung-Yue Liew (Universiti Tunku Abdul Rahman, Malaysia)

Proceedings Chair

  • James Bailey (University of Melbourne, Australia)

Tutorial Chair

  • Hui Xiong (Rutgers, USA)

paper submission

Each paper should consist of a cover page with title, authors' names, postal and email address, and an abstract with up to 200-words, up to 5 keywords and a body not longer than 12 single-spaced pages with font size10 pt, and put both the cover page and the main body into one file. Authors are strongly encouraged to use Springer LNCS/LNAI manuscript submission guidelines for their initial submissions. All papers must be submitted electronically in PDF format only, using the conference management tool. Detailed instructions will be available at the conference website.

The submitted papers must not be previously published anywhere, and must not be submitted to any other conferences before and during the PAKDD review process. A journal submission may be concurrent, but would be expected to have significant additional material not in the conference submission, and the final revision should not have been submitted until the PAKDD reviews have been made available to the authors. Ideally the final journal version should be prepared after the conference so that feedback from the conference can be included. Submitting a paper to the conference means that if the paper were accepted, at least one author will attend the conference to present the paper. For no-show authors, their affiliations will receive a notification.

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. Papers that do not comply with the Submission Guidelines will be rejected without review.

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

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