On Performance Evaluation of Mining Algorithm for Multiple-Level Association Rules based on Scale-up Characteristics
The applications of computers, database technologies and automated data collection techniques require large amount of data to be stored into databases. It, thus, becomes necessary to analyze this data and turn it into useful knowledge. Data mining or Knowledge Discovery in Database (KDD) emerges as a solution to the data analysis problem. One of the data mining techniques that is used to discover interesting rules or relationships among attributes in databases is the association rules. These rules help in discovering knowledge at multiple conceptual levels, which, in turn, provide a spectrum of understanding, from general to specific, for the underlying data. Mining association rules from large data sets has been a focused topic in recent research into knowledge discovery in databases.