日本データベース学会の皆様 PAKDD2017(The Pacific-Asia Conference on Knowledge Discovery and Data Mining) 2017/5/23-26 済州島（韓国）http://pakdd2017.snu.ac.kr/ の締切が10/30に迫ってきました。 皆様の多数のご投稿をお願いできれば幸いです。 山名＠早稲田 ------------------------------- PAKDD2017 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. Important Dates Paper submission due: 23:59:59. PST, Oct 30 (Sun), 2016 Notification to authors: Jan 13 (Fri), 2016 Camera-ready due: Feb 13 (Mon), 2017 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. --------------------------------------------------------------