Abstract
The discovery of association rules is one of the very important tasks in data mining. Association rules help in the generation of more general and qualitative knowledge which in turn helps in decision making. Association rules deal with transactions of both binary values and quantitative data.[9] The traditional algorithms for mining association rules are built on binary attributes databases, which has two limitations. Firstly, it can not concern quantitative attributes; secondly, it treats each item with the same significance although different item may have different significance[6]. Also binary association rules suffers from sharp boundary problems[18]. Moreover many real world transactions consist of quantitative attributes. That is why several researchers have been working on generation of association rules for quantitative data. This paper presents different algorithms given by various researches to generate association rules among quantitative data. We have done comparative analysis of different algorithms for association rules based on various parameters.
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