Affiliations: [a] Department of Computer Science and Information Engineering, National University of Kaohsiung, Taiwan | [b] Department of Computer Science and Information Engineering, Tamkang University, Taiwan
Note: [1] This paper is an extended version of our paper ``A generic
approach for mining indirect association rules in data streams'' presented at
IEA/AIE 2011.
Abstract: An indirect association refers to an infrequent
itempair, each item of which is highly co-occurring with a frequent itemset
called a ``mediator''. Although indirect associations have been recognized
as powerful patterns revealing interesting information hidden in many
applications, such as recommendation ranking, substitute items or
competitive items, common web navigation paths, etc., all work conducted up
to date has focused on mining indirect associations from static data; almost
no work, to our knowledge, has investigated how to discover this type of
pattern from streaming data. This study considers the problem of mining
indirect associations from data streams. Unlike contemporary research work
on stream data mining that has investigated the problem by looking at
different types of streaming models, we treat the problem in a generic way.
We propose a generic window model that can represent all classical streaming
models and retain user flexibility in defining new models. In this context,
a generic algorithm is developed, which guarantees no false positive rules
and bounded support errors as long as the window model is specifiable by the
proposed generic model. Comprehensive experiments on both synthetic and real
datasets show the effectiveness of the proposed approach as a generic way
for finding indirect association rules within streaming data.
Keywords: Indirect association, data stream mining, generic window model
