An Optimized Weighted Association Rule Mining On Dynamic Content
Association rule mining aims to explore large
transaction databases for association rules.
Classical Association Rule Mining (ARM)
model assumes that all items have the same
significance without taking their weight into
account. It also ignores the difference between
the transactions and importance of each and
every itemsets. But, the Weighted Association
Rule Mining (WARM) does not work on
databases with only binary attributes. It makes
use of the importance of each itemset and
transaction. WARM requires each item to be
given weight to reflect their importance to the
user. The weights may correspond to special
promotions on some products, or the profitability
of different items.
This research work first focused on a weight
assignment based on a directed graph where
nodes denote items and links represent
association rules. A generalized version of HITS
is applied to the graph to rank the items, where
all nodes and links are allowed to have weights.
This research then uses enhanced HITS
algorithm by developing an online eigenvector
calculation method that can compute the results
of mutual reinforcement voting in case of
frequent updates. For Example in Share Market
Shares price may go down or up. So we need to
carefully watch the market and our association
rule mining has to produce the items that have
undergone frequent changes. These are done by
estimating the upper bound of perturbation and
postponing of the updates whenever possible.
Next we prove that enhanced algorithm is more
efficient than the original HITS under the context
of dynamic data.
Keywords: Association Rule Mining, Weighted
Association Rule Mining, HITS, Online HITS,
Dynamic Content
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