A Complete Study of Algorithm Selection in Data Mining
N. Krishnaveni, R.Waheetha
Abstract
N. Krishnaveni, R.Waheetha
Abstract
Data mining involves the use of sophisticated data analysis tools to discover previously unknown, valid patterns and relationships in large data set. These tools can include statistical models, mathematical algorithm and machine learning methods. Consequently, data mining consists of more than collection and managing data, it also includes analysis and prediction. This paper puts forward the most used data mining algorithms used in the research field. With each algorithm, a basic explanation is given with a real time example, and each algorithms pros and cons are weighed individually. These algorithms are seen in some of the most important topics in data mining research and development such as classification, clustering, statistical learning, association analysis, and link mining.
A significance statement is not available in the OpenAlex record.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
Data mining involves the use of sophisticated data analysis tools to discover previously unknown, valid patterns and relationships in large data set. These tools can include statistical models, mathematical algorithm and machine learning methods. Consequently, data mining consists of more than collection and managing data, it also includes analysis and prediction. This paper puts forward the most used data mining algorithms used in the research field. With each algorithm, a basic explanation is given with a real time example, and each algorithms pros and cons are weighed individually. These algorithms are seen in some of the most important topics in data mining research and development such as classification, clustering, statistical learning, association analysis, and link mining.
Key concepts: Data mining, Computer science, Cluster analysis, Field (mathematics), Selection (genetic algorithm), Data stream mining, Association rule learning, Set (abstract data type)