Mining Minimal Constrained Flow Cycles from Complex Transaction Data

Xu, M. and Bain, M.

    Transaction data in domains such as trading records from online financial or other markets, logistics delivery registers, and many others, are being accumulated at an increasing rate. In this type of data each transaction has a complex format, being usually associated with attributes such as time, numerical quantity, parties involved, and so on. Performing data mining on trace record data of complex transactions may enable the extraction of knowledge about implicit relationships which will benefit the community in different ways, for example by improving market efficiency and oversight, or detecting scheduling bottlenecks. However, the size of data sets of this type is usually enormous, and therefore in order to perform searching or mining techniques considerations of efficiency are often more important than correctness. In this paper we develop a framework to embed different methods to speed up search algorithms with the goal of detecting cycles in trace record data that _t a given constraint predicate on the amount of transaction quantities that can flow in a direction. The method is shown to improve significantly on a naive approach, and suggests a number of directions for further work.
Cite as: Xu, M. and Bain, M. (2009). Mining Minimal Constrained Flow Cycles from Complex Transaction Data. In Proc. Australasian Data Mining Conference (AusDM`09 ) Melbourne, Australia. CRPIT, 101. Kennedy P. J., Ong K. and Christen P. Eds., ACS. 151-158
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