Objectives

Due to an explosion of data, there has been an increasing demand for scalable machine learning and data mining algorithms in many applications, such as social network analysis, information retrieval, recommendation system, biology applications, multimedia, and e-commerce. The objective of this special issue is to connect academia and industry on the methods and experiences of large scale data analysis. We look for scalable machine learning, data mining algorithms, implementations, frameworks and case studies that target at real and practical scenarios for large datasets. The focus is to identify the real challenges in large-scale data mining and to investigate the scalable methods and practical solutions of the core machine learning and data mining problems with respect to both theoretical and experimental perspectives.

Topics of Interests

• Statistical learning over large-scale data;
• Scalable learning theory;
• Fast inference algorithm;
• Graph-based learning;
• Efficient learning over large streaming data;
• Principle component analysis/ Latent semantic indexing for large-scale data;

• Large-scale social network analysis;
• Pattern discovery from large databases;
• Clustering/classifying large databases;
• Scalable data mining over multiple (heterogeneous) data sources;
• Mining systems in finance, sciences, retail, e-commerce, etc.
• Empirical study of data mining algorithms and applications;
• Distributed data mining algorithms and systems such as Map-Reduce/Hadoop, MPI.

Submissions

On-Line Submissions:
http://www.easychair.org/conferences/?conf=ldmta10
Each paper will be peer-reviewed by at least three reviewers.

Important Dates

Contact us

Yan Liu< liuya@us.ibm.com>