Mining negative sequential patterns from infrequent positive sequences with 2-level multiple minimum supports

Ping Qiu, Long Zhao, Weiyang chen, Tiantian Xu, Xiangjun Dong


Negative sequential patterns (NSP) refer to both occurring items (positive items) and non-occurring items (negative items) and sometimes play a very important role in real applications. Very few NSP methods have been proposed and most of them only mine NSP from frequent positive sequences, not from infrequent positive sequences (IPS). In fact, many useful NSP can be mined from IPS, just like many useful negative association rules can be obtained from infrequent itemsets. e-NSPFI is a method to mine NSP from IPS, but its constraint to IPS is very strict and many useful NSP would be missed. In addition, e-NSPFI only uses single minimum support to mine NSP, which premise is that all items are similar frequencies in the database. In order to solve the above problems and optimize NSP mining, a 2-level multiple minimum supports (2-LMMS) constraint to IPS is proposed in this paper. Firstly, two minimum supports are assigned for each item to constrain frequent and infrequent positive sequences. Secondly, we introduce the method SAP to select actionable NSP. Finally, we propose a corresponding algorithm msNSPFI to mine actionable NSP from IPS with 2-LMMS. Experiment results show that msNSPFI is very efficient for mining actionable NSP.

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