Data mining is used in various forms in different fields of today’s world. Numerous industries have now begun using data mining to compete with the present data analysis environment. In order to acquire easy and quick evaluations of patterns and trends of the prevalent market and to produce useful and fast market trend analysis, different mining techniques and tools are used. Let us now learn about the utilisation of data mining in various industries.
Data Mining for Telecommunication Industry
Telecommunication industry has been advancing very rapidly as technology progresses. The telecommunication industry has grown from local and global telephone services to deliver many other comprehensive communication services like internet access, email, cellular phones and much more.
The telecommunication services have integrated with network, internet and computer and with various other communication technologies. The techniques of data mining can be integrated with these technologies to deliver effective results. The data mining techniques are used to identify fraud activities, detect patterns and also helps for the better utilisation of resources and enhance the quality of services. Below here are the examples of which data mining enhances the telecommunication services.
Analysis of telecommunication data in various dimensions.
Analysis of fraudulent pattern.
Determining unusual patterns.
Analysis of sequential patterns and multidimensional association.
Mobile telecommunication services.
Data Mining for Retail Industry
In today’s world, data mining has a significant impact in the retail industry because it provides huge amounts of data available on services and consumption, goods transportation, customer purchasing history and sales. It is quite natural that the amount of data accumulated will continue swiftly due to the increasing popularity, availability and ease of the e-commerce or web.
Retail data mining is useful in determining the distribution policies, shopping patterns and behaviour of the customer. As retail data is of huge quantity, we design the data warehouse in order to store and conduct a thorough analysis of the data.
The major decision that has to be taken while designing the data warehouse is preprocessing, level and dimension to perform the efficient and quality data mining. Below here are the examples of data mining in the retail industry.
Analysis of sales campaigns’ effectiveness.
Multidimensional analysis of region and time, products, customers and sales.
Data Mining as the Analysis of Financial Data
Financial data is collected mainly from banks and various other financial sectors. This data is usually of very high quality, quite reliable and complete. For data analysis, financial data requires a systematic method. Data mining plays a significant role in financial data analysis. Data mining follows steps like model deployment and evaluation, model building, data refinement, data collection and understanding of data. In financial institutes like banks, the available data is of premium quality and reliable. This helps data mining and systematic data analysis. Data mining is used to predict profitability and trend, estimate the risk and predict credit card fraud in the banking field. Data mining technique like neural networks is utilised in price prediction, stock forecasting and so on in the financial markets.
Few of the specific cases are Identification of money laundering and various other financial crimes, clustering and classification of customers for the purpose of targeted marketing, analysis of particular customer credit policy and prediction of loan payments by creditors.
Data Mining for Science and Engineering
Numerous scientific data analysis tasks managed homogeneous and small data sets in the past. Those data sets were analysed making use of a “formulate build, hypothesis model and assess the results” paradigm. The statistical techniques were employed typically for their analysis in these cases. Huge storage collections and data collection have transformed the scientific data analysis landscape recently. The scientific data can be analysed today at much lesser costs and quicker speeds.
This has resulted in the collection of large volumes of stream data and high dimensional data consisting of spatial rich information. As a result, there is a shift in the scientific applications from the “test and hypothesise” paradigm towards a “gather and reserve data, mine for new hypotheses and confirm with experimentation or data” process. A special type of data is generated by computer science. For instance, the computer programs can be long and many times, their execution generates large data traces. In computer science, data mining can be utilised to help recognise system malfunctions, uncover the network intrusions, analysing faults in the computer system, detect software plagiarism, isolate software bugs, improve system performance and monitor system status.
Future of Data mining techniques
In future, the data mining techniques can be integrated with cloud computing frameworks like Spark, MapReduce, Hadoop etc for Big Data Analytics to assist in different applications such as driving association patterns that are useful for E-Commerce and retail scenario, adaptive web page ranking systems and so on.