IEEE Task Force on Data Mining in Industrial Applications
Data mining has been used extensively in many industrial and manufacturing applications such as engineering fault detection, diagnosis and prognosis, modeling of market trends and consumer shopping patterns, real-time process monitoring and control, soft sensors, supply-chain management, industrial process optimization, and so on. Data mining in industrial applications often involve many challenges, including data noise, incomplete and inconsistent data, large volumes of data, complex patterns, and application-specific constraints. For examples text documents collected for diagnostics are often verbatim descriptions that contain many grammatical errors, self invented acronyms and terminologies that are not seen in news articles or other published articles. This task force is formed to focus on the development of the data mining technologies for these application problems.
The main goal of this task force is to promote and disseminate data mining technologies and systems that are innovative and effective in the application areas stated above. The task force will provide forums for researchers and practitioners to exchange research ideas, share results, and quickly disseminate new technologies to researchers and practitioners.
The scope of this task force includes the following topics:
- Text mining from unstructured and informal descriptions
- Building ontologies based on domain specific knowledge for text mining
- Data mining from noisy data
- Mining knowledge from spatial and time domain data
- Data mining methods based on statistical techniques such as correction and causality modeling
- Regression techniques for inferring process parameters from other available variables
- Text document classification techniques