ICDM 2013: IEEE International Conference on Data Mining December 8-11, 2013, Dallas, Texas.
Dates:
- Workshop proposals: April 2
- Workshop notification: April 30
- ICDM contest proposals: April 30
- Full paper submissions: June 21
- Demo and tutorial proposals: August 3
- Workshop paper submissions: August 3
- Conference paper, tutorial, demo notifications: September 20
- Workshop paper notifications: September 24
- Conference dates: December 8-11 (Sunday-Wednesday)
From the call for papers:
The IEEE International Conference on Data Mining (ICDM) has established itself as the world's premier research conference in data mining. The 13th ICDM conference (ICDM '13) provides a premier forum for the dissemination of innovative, practical development experiences as well as original research results in data mining, spanning applications, algorithms, software and systems. The conference 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 high quality and novel research findings, and innovative solutions to challenging data mining problems, the conference seeks to continuously advance the state of the art in data mining. As an important part of the conference, the workshops program will focus on new research challenges and initiatives, and the tutorials program will cover emerging data mining technologies and the latest developments in data mining.
Topics of Interest
Topics related to the design, analysis and implementation of data mining theory, systems and applications are of interest. These include, but are not limited to the following areas:
- Foundations of data mining
- Data mining and machine learning algorithms and methods in traditional areas (such as classification, regression, clustering, probabilistic modeling, and association analysis), and in new areas
- Mining text and semi-structured data, and mining temporal, spatial and multimedia data
- Mining data streams
- Mining spatio-temporal data
- Mining with data clouds and Big Data
- Link and graph mining
- Pattern recognition and trend analysis
- Collaborative filtering/personalization
- Data and knowledge representation for data mining
- Query languages and user interfaces for mining
- Complexity, efficiency, and scalability issues in data mining
- Data pre-processing, data reduction, feature selection and feature transformation
- Post-processing of data mining results
- Statistics and probability in large-scale data mining
- Soft computing (including neural networks, fuzzy logic, evolutionary computation, and rough sets) and uncertainty management for data mining
- Integration of data warehousing, OLAP and data mining
- Human-machine interaction and visual data mining
- High performance and parallel/distributed data mining
- Quality assessment and interestingness metrics of data mining results
- Visual Analytics
- Security, privacy and social impact of data mining
- Data mining applications in bioinformatics, electronic commerce, Web, intrusion detection, finance, marketing, healthcare, telecommunications and other fields
I saw a post recently that made the case for data mining being the next “hot” topic in cybersecurity.
As in data mining that can track you across multiple social media sites, old email posts, etc.
Curious that it is always phrased in terms of government or big corporations spying on little people.
Since there are a lot more “little people,” shouldn’t crowd sourcing data mining of governments and big corporations work the other way too?
And for that matter, like the BLM (Bureau of Land Management), there really isn’t any “government,” or “government agency” that is responsible for harm to the public’s welfare.
There are specific people with relationships to the oil and gas industry, meetings, etc.
Let’s use data mining to pierce the government veil!