Abstract:
Association Rules is the process of identifying
relationships among set of items in transaction
database. Finding frequent itemsets is the most
expensive step in Association rule discovery. Real
world datasets are sparse, dirty and contain
hundreds of items. In such situations, discovering
interesting rules (results) using traditional frequent
itemset mining approach is not appropriate. In this
paper, mining association rules for Transaction
database using FP-Growth and DynFP-Growth
algorithms is presented. FP-Growth algorithm
avoids or reduces candidate generation. Moreover, it
greatly reduces the need to traverse the database.
DynFP-Growth algorithm works in the same process
as FP-Growth with lexicographic order, and stores
at least one item is detected. Although resulting FPTree
is too large to store in memory, it is more
flexible with different support values. For the
empirical study, three synthetic datasets by IBM
Quest data generator is used: (a) T10I4D100K (b)
T40I10D30K, and (c) T40I10D100K.
