Explain Data Mining Trends.

 Data Mining Trends

The diversity of data, data mining tasks, and data mining approaches pose many challenging research issues in data mining. The development of efficient and effective data mining methods, systems and services, and interactive and integrated data-mining environments is a key area of study. The use of data mining techniques to solve large or sophisticated application problems is an important task for data mining researchers and data mining systems and application developers. 

This section describes some of the trends in data mining that reflect the pursuit of these challenges.

Application exploration: 

Early data mining applications put a lot of effort into 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 in the retail industry. Data mining is increasingly used for the exploration of applications in other areas such as web and text analysis, financial analysis, industry, government, biomedicine, and science. Emerging application areas include data mining for counterterrorism and mobile (wireless) data mining. Because generic data mining systems may have limitations in dealing with application-specific problems, we may see a trend toward the development of more application-specific data mining systems and tools, as well as invisible data mining functions embedded in various kinds of services.

Scalable and interactive data mining methods: 

In contrast with traditional data analysis methods, data mining must be able to handle huge amounts of data efficiently and, if possible, interactively. Because the amount of data being collected continues to increase rapidly, scalable algorithms for individual and integrated data-mining functions become essential. One important direction toward improving the overall efficiency of the mining process while increasing user interaction is constraint-based mining. This provides users with added control by allowing the specification and use of constraints to guide data mining systems in their search for interesting patterns and knowledge.

Integration of data mining with search engines, database systems, data warehouse systems, and cloud computing systems: 

Search engines, database systems, data warehouse systems, and cloud computing systems are mainstream information processing and computing systems. It is important to ensure that data mining serves as an essential data analysis component that can be smoothly integrated into such an information processing environment. A data mining subsystem/service should be tightly coupled with such systems as a seamless, unified framework or as an invisible function. This will ensure data availability, data mining portability, scalability, high performance, and an integrated information processing environment for multi-dimensional data analysis and exploration.