IEEE International Conference on Data Mining 2009

December 6-9, 2009 Miami, U.S.A.

The IEEE International Conference on Data Mining (ICDM) has established itself as the world's premier research conference in data mining. The 2009 edition of ICDM provides a leading forum for presentation of original research results, as well as exchange and dissemination of innovative, practical development experiences.

The conference covers all aspects of data mining, including algorithms, software and systems, and applications. In addition, ICDM draws researchers and application developers from a wide range of data mining related areas such as statistics, machine learning, pattern recognition, databases and data warehousing, data visualization, knowledge-based systems, and high performance computing.

By promoting novel, high quality research findings, and innovative solutions to challenging data mining problems, the conference seeks to continuously advance the state-of-the-art in data mining.

Besides the technical program, the conference will feature workshops, tutorials, panels, and the ICDM data mining contest.

Topics of Interest

• Data mining foundations
• • Novel data mining algorithms in traditional areas (such as classification, regression, clustering, probabilistic modeling, pattern discovery, and association analysis)
  • Models and algorithms for new, structured, data types, such as arising in chemistry, biology, environment, and other scientific domains
  • Developing a unifying theory of data mining
  • Mining sequences and sequential data
  • Mining spatial and temporal datasets
  • Mining textual and unstructured datasets
  • Distributed data mining
  • High performance implementations of data mining algorithms
  • Privacy and anonymity-preserving data analysis
• Mining in emerging domains
• • Stream data mining
  • Mining moving object data, RFID data, and data from sensor networks
  • Ubiquitous knowledge discovery
  • Mining multi-agent data
  • Mining and link analysis in networked settings: web, social and computer networks, and online communities
  • Mining the semantic web
  • Data mining in electronic commerce, such as recommendation, sponsored web search, advertising, and marketing tasks
• Methodological aspects and the KDD process
• • Data pre-processing, data reduction, feature selection, and feature transformation
  • Quality assessment, interestingness analysis, and post-processing
  • Statistical foundations for robust and scalable data mining
  • Handling imbalanced data
  • Automating the mining process and other process related issues
  • Dealing with cost sensitive data and loss models
  • Human-machine interaction and visual data mining
  • Integration of data warehousing, OLAP and data mining
  • Data mining query languages
  • Security and data integrity
• Integrated KDD applications, systems, and experiences
• • Bioinformatics, computational chemistry, ecoinformatics
  • Computational finance, online trading, and analysis of markets
  • Intrusion detection, fraud prevention, and surveillance
  • Healthcare, epidemic modeling, and clinical research
  • Customer relationship management
  • Telecommunications, network and systems management
  • Sustainable mobility and intelligent transportation systems

Important Dates

• April 13, 2009 - Deadline for workshop proposals
• June 26, 2009 - Deadline for paper submission, tutorial submission, and panel proposals
• September 4, 2009 - Notification to authors
• September 28, 2009 - Deadline for camera-ready copies
• December 6-9, 2009 Conference

Information Access vs. Information Retrieval

Jose Maria Gomez publishes a very interesting post about the differences of Information Access and Information Retrieval that are not so clear for a lot of people, including researchers of areas distant from IR or IA.

The Future of Social Networks

Before Christmas, I wrote a post in my Spanish blog Sistemas Inteligentes (Intelligent Systems) containing some reflexions about the future of Social Networks. I try to resume the main idea and translate to English in this post, as I think Social Networks, and all Social Media are a really interesing field to KDD.

As stated by Wikipedia, "Emergence is the way complex systems and patterns arise out of a multiplicity of relatively simple interactions. Emergence is central to the theories of integrative levels and of complex systems". Some strong ideas in AI are connected with emergence, like Swarm Intelligence that "is a type of artificial intelligence based on the collective behavior of decentralized, self-organized systems. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems".


But now, what does emergence have in common with Social Networks? For me there is a clear similarity: both emergent systems and social networks present a group of individuals interacting among them to form something bigger. In emergent systems that group of, let's say stupid or limited, individuals are able to connect among themselves to create some kind of cooperative organism that is more intelligent than the union of the intelligences of the individuals. In today's Social Networks (refering to the Social Networks applications like Facebook or MySpace) we have a really better initial state, we have a group of intelligent individual cooperating among them, but the result is not the expected, because the global information is just the union (or even less, as some information may be duplicated) of the information generated by each user. But it's even worse, the global intelligence of the system is almost null, as today's Social Network systems are all about information and are not trying to create a superior layer of the system by processing all that information and creating real knowledge.

For me, it's clear that the future of Social Networks is about developing systems that generates added value to the users by processing all the information and connexions. Next years, Social Networks will need to use KDD techniques and I'me sure Social Media will become the next big application field for KDD and Machine Learning.