Mining of Association Rules

Mining of sub association rules association rules from databases has attracted great interest because of its potentially very useful applications. sub association rules Association rules are derived from a type of analysis that extracts information from coincidence [ aut Blaxton, T. Blaxton and aut Westphal, C. Westphal, 1998]. Sometimes called sub market basket analysis market basket analysis, this methodology allows a data analyst to discover correlations, or co-occurrences of transactional events. In the classic example, consider the items contained in a customer’s shopping cart on any one trip to a grocery store. Chances are that the customer’s own shopping patterns tend to be internally consistent, and that he/she tends to buy certain items on certain days. There might be many examples of pairs of items that are likely to be purchased together. This is the kind of information the store manager could use to make decisions about where to place items in the store so as to increase sales. This information can be expressed in the form of sub association rules association rules. Such information may have tremendous potential on the marketing of new or existing products. This is the kind of approach used by many enterprises (such as Amazon.com for instance) to recommend new or existing products to their customers. Mining of sub association rules association rules is applicable to many more domains [ aut Bayardo Jr., R.J. Bayardo, et al., 1999]. This chapter is based on the results discussed in [ aut Yilmaz, E. Yilmaz, et al., 2003].