Data mining is increasingly popular in health care. It can help insurers find fraud and abuse, as well as help patients receive better, affordable healthcare. Data mining in health care helps organizations to make management decisions, and can help physicians identify the best treatments. It can also help health care providers to build a pattern that can be turned into a predictive model for the future.
CHARECTERISTICS OF DATA MINING CHARECTERISTICS OF DATA MINING
mining the data is called data mining. Mining the text is called text mining
Some seminar topics related to data mining could include: Introduction to data mining techniques and algorithms Applications of data mining in business intelligence Big data analytics and data mining Ethical considerations in data mining and privacy protection.
Data Mining companies provide such services as mining for data and mining for data two electric bugaloo. They will often offer to resort to underhanded tactics to mine said data.
Data mining can uncover interesting patterns. Some cookies will upload solely for the purpose of data mining.
Data warehouse is the database on which we apply data mining.
Simply, Data mining is the process of analyzing data from several sources and converting it into useful data.
One can learn about data mining by visiting the data mining wikipedia page, which has a very comprehensive article about the topic, starting with the etymology and mostly talking about the various uses of data mining.
Data mining
difference between Data Mining and OLAP
Directed data mining involves using predefined goals or objectives to guide the analysis and modeling of data. In contrast, undirected data mining aims to discover patterns or relationships in data without specifying a particular outcome in advance. Directed data mining is typically used for tasks such as classification and regression, while undirected data mining techniques include clustering and anomaly detection.
Some different types of data mining include clustering, classification, regression, association rule mining, and anomaly detection. Clustering involves grouping similar data points together, while classification involves categorizing data into predefined classes. Regression predicts a continuous value based on input variables, and association rule mining uncovers patterns in data sets. Anomaly detection identifies unusual or outlier data points.