What technologies are used by big data engineers for data mining
[Introduction] Data mining technology helps professionals understand the available data sets. With the development of the big data industry, data analysis and data mining penetrate into our work and life. These technologies can provide descriptive and predictive information for enterprises and other organizations. Capabilities, these capabilities are also a must-have as a big data engineer, so what technologies do big data engineers use for data mining? Next, let’s take a look.
1. Classification
We can use multiple attributes to mark items of a specific category. Classification assigns items to target categories or classes in order to accurately predict what will happen within that class.
Some industries classify customers. For example, a credit company can use a classification model to determine the low, medium, or high credit risk of a loan applicant. Other organizations divide current and target audiences into different age and social groups for marketing activities.
2. Association rules
Association rules make the connection between two or more items to determine the pattern between them. For example, a supermarket can make sure that customers often buy whipped cream when buying strawberries, and vice versa. Associations are often used in point-of-sale systems to determine common trends between products.
Application areas include physical placement organization of items, marketing, and product cross-selling and upsell.
3. Decision tree
Decision trees are used to classify or predict data. The decision tree starts with a simple question, it has two or more answers. Each answer will lead to further questions, which can be used to classify or identify data that can be further classified, or predictions can be made based on each answer.
4. Clustering
Clustering is a method of grouping data records together. This is usually done to give end users a high-level understanding of what is happening in the database.
Viewing the grouping of objects can help companies in market segmentation. In this example, clustering can be used to segment the market into customer subsets. Then, each subset can formulate specific marketing strategies based on the attributes of the clusters, such as comparing the purchasing patterns of customers in one cluster with those in another cluster.
The above is the relevant introduction of data mining technology for big data engineers. I believe it will be helpful for those who want to work in the big data industry. The future development prospects of the big data industry are broad. If you don’t know how to choose, then hurry up and learn about big data. Technology.