18 Important Applications of Data Mining
18 Important Applications of Data Mining: The data mining is a technique that is used to extract the useful information from raw data that are usually hidden and could be revealed using mining methods. Data mining provides several applications to users which are as follows, such as.
18 Important Applications of Data Mining
- Market analysis: This area includes various fields of market that adopted the data mining as prime feature. Such as customer interaction in which mining determines the nature of people to buy which kind of product. Other is examining customer needs by using prediction methods to find factors that attract the most customers. Next is cross market examination that uses correlation between sales. Other factor of mining is to complete target marketing to explore clusters of customers who have same interests, habits and income and so on. Data mining also provides customer purchasing patterns that involve pattern recognition. It also provides multidimensional reports.
- Risk management: data mining also incorporates with risk management area and other corporate areas such as asset evaluation which includes cash flow examine, prediction, claim analysis etc. it also involves comparing the resources and spending of firms also helps to monitor market directions.
- Fraud detection: Data mining is also used in credit card services and to detect fraud calls to find unidentified calls, duration and time of call and so on. It also examines the patterns from expected nominations.
- Weather forecast: it’s a popular area in which mining is widely used. Identification and determination of sky objects are also done with the help of mining.
- Healthcare field: in the field of medicines, mining is used to foresee the developed medicines. Mining is also used to identify certain diseases and treatment methods.
- CHAID: Chi Square Automatic Interaction Detection is a decision tree method for the classification of data set. It provides set of rules to predict record outcomes.
- Artificial neural networks: mining is used in Non-linear predictive models that help in training and resemble biological neural networks.
- Genetic algorithms: various optimization techniques such as genetic mutation, combination, selection works by using various data mining methods.
- Multidimensional databases: data mining is used to design such as database for online analytical processing that has a multidimensional hypercube structure.
- Transportation: various transportation firms can apply data mining on their karge amount iof data to explore best prospects. Data mining is used to examine the customer experience, segments the attributes of high value.
- Data mining also provide standing an adhoc reports for physicians, nurses, and other decision makers to examine clinical management and other resources.
- Data mining also helps in intelligence population characteristics, drug positioning, physician characteristics, regional preferences and many more.
- Data mining tools also help in customer’s evaluated data by structuring the actions and positive results are returned by providing visibility into dimensions of company business.
- Banking: data mining helps in loan information and credit planning and reporting. By Evaluating a model from past data, bank , customer data, institution can help in determining good and wrong loan statements.
- Manufacturing: data mining also indulge into the area of manufacturing like engineering data, and detect faulty material and examine optimal parameters. For example, semiconductor manufacturers have a challenge that even the conditions of manufacturing environments at different wafer production plants are similar.
- Government: data mining helps by mining and examining the records and details of final transactions to build methods or patterns to detect money laundering and other criminal activities.
- Data mining used in Retail stores to detect customer shopping habits/details to optimize the layout of stores to improve customer experience and increase profits.
- Data mining is used to create accurate risk models for loans and mortgages for financial institutions. They are also very helpful when detecting fraudulent transactions.
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