Autor/Autorin: Duraisamy Sasikala
Dr. D. Sasikala completed her undergraduate degree in Computer Science at Bharathiyar University,Coimbatore in 2000 and she has 17 years of teaching experience. She completed her Ph.D in 2016. Currently she is working as Associate Professor in the Department of CSE at Bannari Amman Institute of Technology, Sathy, Erode.
Data mining is a process of extracting hidden information from large databases. It is a powerful technology that helps to analyze the important information present in the databases. Association Rule Mining (ARM) is a research field in data mining that extracts frequent patterns of items in the transaction databases. In order to select interesting rules from the set of all possible rules, various measures are used. The two basic interestingness measures in ARM are support and confidence. In addition to market basket analysis association rules are applied in the areas including web usage mining, intrusion detection, and bioinformatics.
Duraisamy, SasikalaDr. D. Sasikala completed her undergraduate degree in Computer Science at Bharathiyar University,Coimbatore in 2000 and she has 17 years of teaching experience. She completed her Ph.D in 2016. Currently she is working as Associate Professor in the Department of CSE at Bannari Amman Institute of Technology, Sathy, Erode.
Über den Autor
Dr. D. Sasikala schloss ihr Bachelor-Studium der Informatik an der Bharathiyar University, Coimbatore, im Jahr 2000 ab und verfügt über 17 Jahre Lehrerfahrung. Sie schloss ihre Promotion im Jahr 2016 ab. Derzeit arbeitet sie als Associate Professor in der Abteilung für CSE am Bannari Amman Institute of Technology, Sathy, Erode.
Klappentext
Data mining is a process of extracting hidden information from large databases. It is a powerful technology that helps to analyze the important information present in the databases. Association Rule Mining (ARM) is a research field in data mining that extracts frequent patterns of items in the transaction databases. In order to select interesting rules from the set of all possible rules, various measures are used. The two basic interestingness measures in ARM are support and confidence. In addition to market basket analysis association rules are applied in the areas including web usage mining, intrusion detection, and bioinformatics.
