4 research outputs found
HUPSMT: AN EFFICIENT ALGORITHM FOR MINING HIGH UTILITY-PROBABILITY SEQUENCES IN UNCERTAIN DATABASES WITH MULTIPLE MINIMUM UTILITY THRESHOLDS
The problem of high utility sequence mining (HUSM) in quantitative se-quence databases (QSDBs) is more general than that of frequent sequence mining in se-quence databases. An important limitation of HUSM is that a user-predened minimum tility threshold is used commonly to decide if a sequence is high utility. However, this is not convincing in many real-life applications as sequences may have diferent importance. Another limitation of HUSM is that data in QSDBs are assumed to be precise. But in the real world, collected data such as by sensor maybe uncertain. Thus, this paper proposes a framework for mining high utility-probability sequences (HUPSs) in uncertain QSDBs (UQS-DBs) with multiple minimum utility thresholds using a minimum utility. Two new width and depth pruning strategies are also introduced to early eliminate low utility or low probability sequences as well as their extensions, and to reduce sets of candidate items for extensions during the mining process. Based on these strategies, a novel ecient algorithm named HUPSMT is designed for discovering HUPSs. Finally, an experimental study conducted in both real-life and synthetic UQSDBs shows the performance of HUPSMT in terms of time and memory consumption
OPR-Miner: Order-preserving rule mining for time series
Discovering frequent trends in time series is a critical task in data mining.
Recently, order-preserving matching was proposed to find all occurrences of a
pattern in a time series, where the pattern is a relative order (regarded as a
trend) and an occurrence is a sub-time series whose relative order coincides
with the pattern. Inspired by the order-preserving matching, the existing
order-preserving pattern (OPP) mining algorithm employs order-preserving
matching to calculate the support, which leads to low efficiency. To address
this deficiency, this paper proposes an algorithm called efficient frequent OPP
miner (EFO-Miner) to find all frequent OPPs. EFO-Miner is composed of four
parts: a pattern fusion strategy to generate candidate patterns, a matching
process for the results of sub-patterns to calculate the support of
super-patterns, a screening strategy to dynamically reduce the size of prefix
and suffix arrays, and a pruning strategy to further dynamically prune
candidate patterns. Moreover, this paper explores the order-preserving rule
(OPR) mining and proposes an algorithm called OPR-Miner to discover strong
rules from all frequent OPPs using EFO-Miner. Experimental results verify that
OPR-Miner gives better performance than other competitive algorithms. More
importantly, clustering and classification experiments further validate that
OPR-Miner achieves good performance