The applications of data mining
Data mining can be applied to a variety of applications in virtually every industry.
- Retailers can deploy data mining to better identify which products people are likely to purchase based on their past buying habits, or which goods are likely to sell at certain times of the year. This can help merchandisers plan inventories and store layouts.
- Banks and other financial services providers can mine data related to their clients’ accounts, transactions, and channel preferences to better meet their needs. They can also gather then analyzed data from their websites and social media interactions to help increase the loyalty of existing customers and attract new ones.
- Manufacturing companies can use data mining to look for patterns in the production process, so they can precisely identify bottlenecks and flawed methods and find ways to increase efficiencies. They can also apply knowledge from data mining to the design of products, and make tweaks based on feedback from customer experiences.
- Educational institutions can benefit from data mining such as analyzing data sets to predict the future learning behaviors and performance of students, and then using this knowledge to make improvements in teaching methods or curricula.
- Health care providers can mine and analyze data to determine better ways of delivering care to patients and cutting costs. With the help of data mining, they can predict how many patients they will need to care for and what type of services those patients will need. In the life sciences, mining can be used to glean insights from massive biological data, to help develop new medicines and other treatments.
- In multiple industries, including health care and retail, you can use data mining to detect fraud and other abuses—much more quickly than with traditional methods for identifying such activities.
The key components of data mining
The process of data mining includes several distinct components that address different needs:
- Preprocessing. Before you can apply data mining algorithms, you need to build a target data set. One common source for data is a data mart or warehouse. You need to perform preprocessing to be able to analyze the data sets.
- Data cleansing and preparation. The target data set must be cleaned and otherwise prepared, to remove “noise,” address missing values, filter outlying data points (for anomaly detection) to remove errors or do further exploration, create segmentation rules, and perform other functions related to data preparation.
- Association rule learning (also known as market basket analysis). These tools search for relationships among variables in a data set, such as determining which products in a store are often purchased together.
- Clustering. This feature of data mining is used to discover groups and structures in data sets that are in some way similar to each other, without using known structures in the data.
- Classification. Tools that perform classification generalize known structures to apply to new data points, such as when an email application tries to classify a message as legitimate mail or spam.
- Regression. This data mining technique tis used to predict a range of numeric values, such as sales, housing values, temperatures, or prices when given a particular data set.
- Summarization. This technique provides a compact representation of a data set, including visualization and report generation.
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