Genetic Algorithms and Rule Induction Analysis
Data mining is a data analyzing process that analyzes the data from different aspects and summarizes it into useful information that can be used to increase revenue and cost cuts (Data Mining: What is Data Mining? 2012). Data mining has different levels of analyzing. Genetic algorithms and rule inductions are two of the six different levels of analysis. Genetic algorithms are techniques that use genetic mutation, combination, and natural selection to analysis data. Rule induction is a way of analyzing data by means of extraction. Some of the attributes that will be discussed concerning genetic algorithms and rule induction are the benefits, limitations, risks of each of the selected techniques, and practical examples of when each technique would be most effectively utilized by a health care organization. Benefits
Data mining would not be a great tool without the parts that it is composed of. Genetic algorithms ad rule inductions are parts of data mining and just happen to have great benefits. The advantages of genetic algorithms are that they can be easily transferred, easy to understand and practical, and different types of problems can be solved (Advantages and disadvantages of genetic algorithms, 2012). Genetic algorithms can be transferred to simulations and models that previously existed. They are easily understood because they do not demand a mathematical background. Some of the problems that can be solved using genetic algorithms are optimization, multiple solutions, and multi-dimensional. Some of the advantages of rule induction are that it enhances the thinking process, it is easily understood, and the possibility of new knowledge can be deduced. The thinking process can be a challenge for some but with rule inductions the thinking processed is enhanced by offering a wide array of ideas. In order to understand rule inductions, an expert does not have to be a knowledge engineer. The new knowledge that...
Please join StudyMode to read the full document