Year: 2020
Type: article
Abstract: Utility-oriented pattern mining is an emerging topic, since it can reveal high-utility patterns from different types of data, which provides more information than the traditional frequency/confidence-based pattern mining models. The utilities of various items/objects are not exactly equal in realistic situations; each item/object has its own utility or importance. In general, the user considers a uniform minimum utility ( minutil ) threshold to identify the set of high-utility sequential patterns (HUSPs). This is unable to find the interesting patterns while the minutil is set extremely high or low. We first design a new utility mining framework namely USPT for mining high-Utility Sequential Patterns across multi-sequences with individualized Thresholds. Each item in the designed framework has its own specified minimum utility threshold. Based on the lexicographic-sequential tree and the utility-array structure, the USPT framework is presented to efficiently discover the HUSPs. With the upper-bounds on utility, several pruning strategies are developed to prune the unpromising candidates early in the search space. Several experiments are conducted on both real-life and synthetic datasets to show the performance of the designed USPT algorithm, and the results show that USPT could achieve good effectiveness and efficiency for mining HUSPs with individualized minimum utility thresholds.
DOI: 10.1145/3362070
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Awards: III: Small: Exploiting the Massive User Generated Utterances for Intent Mining under Scarce Annotations, III: Medium: Collaborative Research: An Extensible Heterogeneous Network Embedding Framework with Application Specific Adaptation, G4573880676, G6760473191, MRI: Acquisition of SABER: Shared Analytics and Big-data Enterprise Resource, III: Small: Fusion of Heterogeneous Networks for Synergistic Knowledge Discovery, SaTC: CORE: Small: Collaborative: Learning Dynamic and Robust Defenses Against Co-Adaptive Spammers
OpenAlex ID: W2997709873