method in data mining The association rule method can be used for some data mining problems. Transaction data, enterprises can identify products that are often purchased together. For example, if customers buy baby diapers, they are likely to also buy baby diapers.电影电影管理。 Recommended system：Online platform uses social rules based on user’s previous purchase or browsing history. For example, online movie streaming media service can recommend specific movies to users who have watched similar movies in the past. This can be done. Personalize user experience and improve participation. Fraud detection: Correlation rules can be used to identify abnormal patterns of fraud in financial transactions. For example, correlation rules may identify a large number of purchases made from a specific location in a short period of time, which may indicate the use of被盗的credit card。 This can help experts further investigate credit card financial activity. Text mining: Another application of the
association rule method is to analyze
text data, such as customer comments, social media posts, or news articles. Identify existing words or themes. The pattern is helpful Rich people number data for author emotional analysis, theme modeling, and content recommendation system. For example, analysis of submitted product reviews may show that words such as “disappointing” and “defective” are often used together. These descriptions indicate the potential problems of the product. Medical research: Correlation rules are used in various medical research to identify the relationship between genes and proteins. These patterns help to identify patients’ symptoms, and can indicate people’s specific diseases. Artificial intelligence robots are helping data scientists analyze data and charts – data mining techniques 9. Neural Networks in Data Mining Neural networks are one of the other data mining techniques, just like brain neurons, which aim to identify relationships in data. Based on neural networks The deep learning algorithm seeks to find hidden patterns in a large amount of data. Application training of artificial neural network——classification, prediction, and combination with progressive algorithm Artificial neural network real-time training video——classification, prediction, and combination with progressive algorithm Click These models have been
criticized for their interpretability
and are rarely used in the data mining field when they appear. However, the advantages of neural networks, such as the ability to Afghanistan Phone Number List manage high noise levels and classify unknown patterns, make them useful in solving a variety of problems. Types of neural network models network models, each model has its own advantages and disadvantages, and according to its unique characteristics, design them to solve specific problems. A common neural network model: The robot is designing an artificial brain with a computer, surrounded by some programming code—— data mining technology “multi-layer sensor” neural network (MLP): This model is considered to be the simplest, most basic neural network. The network is composed of multiple interconnected layers, each layer contains multiple nodes. Each node receives a set of inputs, processes them using activation functions, and sends the output to the neurons of the next layer. The model can be used to solve data. Classification, regression, and prediction etc. problems. Convolutional neural network (CNN) : This model is considered a powerful tool in image processing and object
