Mining Negative Sequential Rules from Negative Sequential Patterns

As an important tool for behavior informatics, negative sequential rules (NSRs, e.g., double right arrow inverted left perpendicular) are sometimes much more informative than positive sequential rules (PSRs, e.g., double right arrow ), as they can provide valuable decision-making information from both negative and positive sides. Very limited NSR mining algorithms are available now and most of them discover NSRs only from positive sequential patterns (PSPs, e.g., ) rather than from negative sequential patterns (NSPs, e.g., ), which may result in a loss of important information. However, discovering NSRs (e.g., double right arrow) from NSPs is much more difficult than mining NSRs from PSPs because NSPs do not satisfy the downward closure property. In addition, it is very difficult to find which kind of NSRs should be generated. This paper proposes a novel algorithm named nspRule to address all these difficulties. The experiment results on real-life and synthetic datasets show that nspRule can mine NSRs correctly and efficiently w.r.t. several aspects including rule number, runtime, memory usage and scalabilit.