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What are the trends in data mining?
The trends in data mining are as follows −
Application exploration − Early data mining applications targeted generally on helping businesses gain a competitive edge. The exploration of data mining for businesses continues to expand as e-commerce and e-marketing have become mainstream components of the retail market.
Data mining is increasingly used for the exploration of applications in several areas, including financial analysis, telecommunications, biomedicine, and science. Emerging software areas contain data mining for counterterrorism (including and beyond intrusion detection) and mobile (wireless) data mining. As generic data mining systems can have limitations in dealing with application-specific issues, it can view a trend toward the development of more application-specific data mining systems.
Scalable and interactive data mining methods − In contrast with traditional data analysis methods, data mining should be able to manage huge amounts of data effectively and, if possible, interactively. Because the amount of information being collected continues to increase rapidly, scalable algorithms for single and integrated data mining services become essential.
One important direction toward improving the completed efficiency of the mining process while increasing customer interaction is constraint-based mining. This supports users with added control by enabling the description and use of constraints to guide data mining systems in their search for interesting patterns.
Integration of data mining with database systems, data warehouse systems, and Web database systems − Database systems, data warehouse systems, and the Web have become mainstream data processing systems. It is essential to provide that data mining serves as an essential data analysis component that can be smoothly integrated into including a data processing environment.
Standardization of data mining language − A standard data mining language or other standardization efforts will support the systematic development of data mining solutions, improve interoperability between multiple data mining systems and services, and promote the education and use of data mining systems in the market and society.
Visual data mining − Visual data mining is an efficient method to find knowledge from huge amounts of data. The systematic study and development of visual data mining methods will support the promotion and use of data mining as a tool for data analysis.
Data mining and software engineering − As software programs become increasingly heavy in size, sophisticated in difficulty, and tend to originate from the unification of multiple components developed by several software teams, it is an increasingly challenging task to provide software robustness and reliability.
The analysis of the executions of a buggy application program is essentially a data mining process tracing the data generated during program executions can disclose important patterns and outliers that can lead to the eventual automated discovery of software bugs.
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