F-NSP+: A fast negative sequential patterns mining method with self-adaptive data storage

TL;DR: This paper verifies the monotonic nondecreasing property of the negative co-location participation index (PI) value as the size increases and deduced that any prevalent negative co,location pattern with size n can be generated by connecting prevalent co- location with size 2 and with an n−1 size candidate negative co -location pattern.

TL;DR: A new algorithm, NegPSpan, is introduced that extracts NSPs using a prefix-based depth-first scheme, enabling maxgap constraints that other approaches do not take into account and can process bigger datasets than eNSP thanks to significantly lower memory requirements and better computation times.

TL;DR: This paper first analyzes what kinds of PNARs are redundant and then proposes a novel method called LOGIC by using logical reasoning to prune redundant PnARs, which is likely to be the first method to prunes positive and negative association rules simultaneously.

TL;DR: Wang et al. as discussed by the authors proposed a negative sequential pattern (NSP) mining method called sc-NSP, which is 10 times more efficient than other state-of-the-art methods.

TL;DR: This work proposes a novel framework for long -duration complex activity prediction by discovering three key aspects of activity: Causality, Context-cue, and Predictability, and presents a predictive accumulative function (PAF) to depict the predictability of each kind of activity.

TL;DR: An efficient algorithm is designed to identify top-k high utility sequential patterns without minimum utility, and three effective features are introduced to handle the efficiency problem, including two strategies for raising the threshold and one pruning for filtering unpromising items.

TL;DR: An extensive experimental study on real datasets shows that TKS outperforms TSP, the current state-of-the-art algorithm for top-k sequential pattern mining by more than an order of magnitude in execution time and memory.

TL;DR: An extensive evaluation on nine real-life datasets of the different ways in which the basic BIDE-Discriminative can be used in real multi-class classification problems, including 1-versus-rest and model-based search tree approaches show that 1-vs-rest provides an efficient solution with good classification performance.