Application exploration: Traditional data mining applications had a great deal of attention on helping business gain well than others of a comparable nature. Data mining is explored to an increasing extent in areas such as financial analysis, telecommunications, biomedicines, science and also for counterterrorism and mobile (wireless) data mining.
Scalable and interactive data mining methods: Data mining must be able to handle large amount of data efficiently and interactively apart from the existing data analysis methods. Constraint based data mining helps user to guide data mining systems in their search for interesting pattern.
Integration of data mining with database systems, data ...view middle of the document...
Data mining and software engineering: In an increasing extend software programs becomes bulky in size and very complex and hence it is now a difficult task to ensure software robustness and reliability. It is further expected to develop data mining methodologies for software debugging to ensure software robustness.
Web mining: One of the great significant and rapidly developing subfields in data mining includes web content mining, weblog mining, data mining services on the internet and the significance of the role played by web in today’s society.
Distributed data mining: Early data mining methodologies did not go well with most of the distributed computing environments like internet, intranets, local area networks, sensor network. Looking forward in the progress of distributed data mining methods
Real-time or time-critical data mining: Dynamic data mining models need to be constructed in real time for applications involving stream data such as e-commerce, web mining, stock analysis, intrusion detection & mobile data mining.
Graph mining, link analysis, and social network analysis: Sequential, topological,...