Applications of Data Mining

Data mining commonly applies to any form of extensive data or information processing like data collection, data extraction, data warehousing, and statistical analysis. It may also include the use of computer-based decision support system, comprising artificial intelligence, statistics, machine learning, database management, and business intelligence. The field has a varied range of application areas, such as—

Business: Different business groups integrate their organizational databases into larger centralized data warehouses for ease of analysis of data by the decision makers. Apart from that, other notable uses of data mining in this area are risk assessment, customer relationship management, price evaluation of specific products, modeling and forecasting credit fraud, and stock price prediction.

Scientific and Technical Research: Data mining applications are in use with areas like scientific and technical research, including electrical engineering, medicine, bioinformatics, genetics, software engineering, and education. 

Spatial Data Mining: This is the use of data mining techniques to spatial data. The spatial data mining tries to discover data patterns with respect to geography. The combination of data mining and Geographic Information Systems (GIS) can accomplish some form of decision-making.

Temporal Data Mining: Data might consist of features or attributes created and noted at different times. In this case discovering meaningful associations in the data might have to consider the temporal order of the features.  

Sensor data mining: Wireless sensor networks (WSNs) are in use to simplify the assembly of data for spatial data mining for applications like air pollution monitoring.


Future Scope

Nowadays data mining is considered to be a very promising field in the information industries and in the research organizations. With recent growth in different aspects of information technology, data mining has become an increasingly significant tool to transform raw data into meaningful knowledge. As real-world databases have developed in size and complexity, data mining has increasingly been improved with unanticipated, computerized data processing, associated with artificial intelligence, machine learning, and statistics. But, in spite of all, there might be some undiscovered patterns that remained untouched so far by the older, existing techniques. So, there is still a gap remains between data and information. The data mining researchers would like to address this issue by devising better methods. And this way the evolution continues.

The research domain related to data mining tries to minimize the gap between data and information by designing new data mining algorithms, techniques or tools. If employed, these methods or tools could be used to discover the new information after analysis. The researchers may transform the gathered information into significant knowledge indicating new future trends. The goal is to invent new learning and knowledge discovery algorithms more proficiently thereby permitting such approaches to use in larger databases. Furthermore, the novel data mining techniques mostly perform innovative data analysis and may reveal valuable information, adding immensely to business policies, knowledge bases, and scientific researches. Therefore, with the present scenario and the forecast of the information technology expansion, there is always a need for new data mining techniques.