Classification and clustering of customers for targeted marketing. Detection of money laundering and other financial crimes. B. Retail Industry 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: Design and Construction of data warehouses based on benefits of data mining. Multidimensional analysis of sales, customers, products, time and region. Analysis of effectiveness of sales campaigns. Customer Retention. Product recommendation and cross-referencing of items. C. Telecommunication 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 as Multidimensional Analysis of Telecommunication data. Fraudulent pattern analysis. Identification of unusual patterns.
ISSN (Online) : 2278-1021 ISSN (Print) : 2319-5940 International Journal of Advanced Research in Computer and Communication Engineering Vol. 3, Issue 10, October 2014 Copyright to IJARCCE 8098 Multidimensional association and sequential patterns analysis. Mobile Telecommunication services. Use of visualization tools in telecommunication data analysis. D. Biological Data Analysis 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: Semantic integration of heterogeneous, distributed genomic and proteomic databases. Alignment, indexing, similarity search and comparative analysis multiple nucleotide sequences. Discovery of structural patterns and analysis of genetic networks and protein pathways. E. Other Scientific Applications 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
- Spring '17
- Data Mining