Clients
Data Mining is widely used in diverse areas. There are number of commercial data mining system available today yet there are many challenges in this field. In this tutorial we will applications and trend of Data Mining.
Data Mining Applications
Here is the list of areas where data mining is widely used:
The financial data in banking and financial industry is generally reliable and of high quality which facilitates the systematic data analysis and data mining. Here are the few typical cases:
Design and construction of data warehouses for multidimensional data analysis and data mining.
Loan payment prediction and customer credit policy analysis.
Classification and clustering of customers for targeted marketing.
Detection of money laundering and other financial crimes.
Data Mining has its great application in Retail Industry because it collects large amount data from on sales, customer purchasing history, goods transportation, consumption and services. It is natural that the quantity of data collected will continue to expand rapidly because of increasing ease, availability and popularity of web.
The Data Mining in Retail Industry helps in identifying customer buying patterns and trends. That leads to improved quality of customer service and good customer retention and satisfaction. Here is the list of examples of data mining in retail industry:
Today the Telecommunication industry is one of the most emerging industries providing various services such as fax, pager, cellular phone, Internet messenger, images, e-mail, web data transmission etc.Due to the development of new computer and communication technologies, the telecommunication industry is rapidly expanding. This is the reason why data mining is become very important to help and understand the business.
Data Mining in Telecommunication industry helps in identifying the telecommunication patterns, catch fraudulent activities, make better use of resource, and improve quality of service. Here is the list examples for which data mining improve telecommunication services:
Now a days we see that there is vast growth in field of biology such as genomics, proteomics, functional Genomics and biomedical research.Biological data mining is very important part of Bioinformatics. Following are the aspects in which Data mining contribute for biological data analysis:
The applications discussed above tend to handle relatively small and homogeneous data sets for which the statistical techniques are appropriate. Huge amount of data have been collected from scientific domains such as geosciences, astronomy etc. There is large amount of data sets being generated because of the fast numerical simulations in various fields such as climate, and ecosystem modeling, chemical engineering, fluid dynamics etc. Following are the applications of data mining in field of Scientific Applications:
Intrusion refers to any kind of action that threatens integrity, confidentiality, or availability of network resources. In this world of connectivity security has become the major issue. With increased usage of internet and availability of tools and tricks for intruding and attacking network prompted intrusion detection to become a critical component of network administration. Here is the list of areas in which data mining technology may be applied for intrusion detection:
There are many data mining system products and domain specific data mining applications are available. The new data mining systems and applications are being added to the previous systems. Also the efforts are being made towards standardization of data mining languages.
Which data mining system to choose will depend on following features of Data Mining System:
Coupling data mining with databases or data warehouse systems - Data mining system need to be coupled with database or the data warehouse systems. The coupled components are integrated into a uniform information processing environment.Here are the types of coupling listed below:
Scalability - There are two scalability issues in Data Mining as follows:
Visualization Tools - Visualization in Data mining can be categorized as follows:
Here is the list of trends in data mining that reflects pursuit of the challenges such as construction of integrated and interactive data mining environments, design of data mining languages: