Overview of Data Mining Applications
Data mining is how the patterns in large data sets are viewed and discovered using intersecting techniques such as statistics, machine learning, and ones like databases systems. It involves data extraction from a group of raw and unidentified data sets to provide some meaningful results through mining.
The extracted data is then further used by using transformation and ensuring that it is being arranged to best service as per business requirements and needs. In this article, we are going to read about the various applications associated with data mining.
List of Data Mining Applications
Here is the list of various Data Mining Applications, which are given below –
1. Financial firms, banks, and their analysis
Many data mining techniques are involved in critical banking and financial data providing and keeping firms whose data is of utmost importance. One such method is distributed data mining, which is researched, modelled, crafted, and developed to help track suspicious activities or any mischievous or fraudulent transactions related to the credit card, net banking, or any other banking service.
By sampling and identifying a huge set of customer data, analysis becomes quite an easy task. Furthermore, tracking suspicious activities becomes a comparatively more straightforward task by keeping a tab of the parameters such as the period of transactions, geographical locations, mode of payments, customer activity history, etc. Based on these parameters, the customer’s relative measure is then calculated, which can be put to any form of usage based on the indices calculated.
The bank can retain their customers or work in acquiring a new set of customers by performing the right set of data mining on the historical data and the nature of their activities. The information plays a huge role in the success and failure of any organization, especially after the advent of big data. The alluring offers can then be launched based on the customer’s activity and historic transacting and purchase trends. The correlation among various financial indicators can be found out by making use of data mining. The patterns and the rise and fall of the stock markets and their predictions can also be analyzed by using data mining.
2. Health care domain and insurance domain
The data mining-related applications can efficiently track and monitor a patient’s health condition and help in efficient diagnosis based on the past sickness record. Similarly, the insurance industry’s growth depends on the ability to convert the data into knowledge form or by providing various details about the customers, markets, and prospective competitors. Therefore all those companies who have applied the data mining techniques efficiently have reaped the benefits. This is used over the claims and their analysis, i.e., identifying the medical procedures claimed together. It enables the forecasting of new policies, helps detect risky customer behaviour patterns, and helps see fraudulent behaviour.
3. Application in the domain of transportation
The historic or batch form of data will help identify the mode of transport a particular customer generally opts for going to a particular place, say his home town, thereby providing him alluring offers and heavy discounts on new products and launched services. This will thus be included in targeted and organic advertisements where the prospective leader of the customer generates the right to converted the lead. It is also helpful in determining the distribution of the schedules among various warehouses and outlets for analyzing load based patterns.
4. Applications of data mining in the field of medicine
In the case of medical analysis, a patient’s case can be analyzed by making a tab of his clinic visits and the season of his holidays. It also helps in the identification of patterns that have successful medical therapies for various kinds of illnesses. Researchers are using multi-dimensional data to reduce costs and improve the quality of services being provided today with extensive and better care. Other approaches, such as soft computing, statistics, data visualization, and machine learning, are used efficiently to gauge and predict patients’ data volume within a single category. The processes are developed to ensure that the patients receive appropriate attention whenever needed. It also helps healthcare and medicine based insurers detect fake and fraud cases.
5. Education
In education, the application of data mining has been prevalent, where the emerging field of educational data mining focuses mainly on the ways and methods by which the data can be extracted from age-old processes and systems of educational institutions. The goal is often provided by making a student grow and learn in various facets using advanced scientific knowledge. Here data mining comes majorly into play by ensuring that the right quality of knowledge and decision-making content is provided to the education departments.
6. Manufacturing Engineering
The data can be assessed by ensuring that the manufacturing enterprise possesses the right set of knowledge as its asset lies in identifying the right set of product portfolios, product architecture, and the customer needs and requirements. Moreover, efficient data mining capabilities can ensure that product development is completed in the relevant time frame and does not exceed the budget allotted initially.
Conclusion
We have studied a basic overview and a layout of various data mining applications in different domains. The scope of this huge and endless technique is not confined to these sectors only but also extends to every part a business can flourish in.
It is only about the right techniques and some analysis to make your usual regular business different from competitors. The world today is running behind data and its management, and efficient handling is the key factor that impacts an organisation’s growth majorly, especially in today’s times. I hope you liked our article. Keep collecting data, analyze them and keep reading our blogs for more technology-related articles.
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