SCOPE
Knowledge Discovery is an interdisciplinary area focusing upon methodologies for identifying valid, novel, potentially useful and meaningful patterns from data, often based on underlying large data sets. A major aspect of Knowledge Discovery is data mining, i.e. applying data analysis and discovery algorithms that produce a particular enumeration of patterns (or models) over the data. Knowledge Discovery also includes the evaluation of patterns and identification of which add to knowledge. This has proven to be a promising approach for enhancing the intelligence of software systems and services. The ongoing rapid growth of online data due to the Internet and the widespread use of large databases have created an important need for knowledge discovery methodologies. The challenge of extracting knowledge from data draws upon research in a large number of disciplines including statistics, databases, pattern recognition, machine learning, data visualization, optimization, and high-performance computing, to deliver advanced business intelligence and web discovery solutions.
Information retrieval (IR) is concerned with gathering relevant information from unstructured and semantically fuzzy data in texts and other media, searching for information within documents and for metadata about documents, as well as searching relational databases and the Web. Automation of information retrieval enables the reduction of what has been called "information overload".
Information retrieval can be combined with knowledge discovery to create software tools that empower users of decision support systems to better understand and use the knowledge underlying large data sets.
The primary focus of KDIR is to provide a major forum for the scientific and technical advancement of knowledge discovery and information retrieval.

CONFERENCE TOPICS
Information extraction
Machine Learning
Concept Mining
Context Discovery
Foundations of knowledge discovery in databases
Data analytics
Optimization
Interactive and online data mining
Process mining
Integration of data warehousing and data mining
Data reduction and quality assessment
Pre-processing and post-processing for data mining
Mining high-dimensional data
Mining text and semi-structured data
Information Extraction from Emails
Mining multimedia data
Web mining
User Profiling and Recommender Systems
Collaborative Filtering
Structured data analysis and statistical methods
BioInformatics & pattern discovery
Clustering and classification methods
Visual data mining and data visualization
Software development
Business intelligence applications
Data mining in electronic commerce