Utility Mining Across Multi-Dimensional Sequences

TL;DR

This paper introduces a novel framework called MDUS for utility mining across multi-dimensional sequences, effectively extracting useful patterns from complex, feature-rich sequential data with auxiliary information.

Contribution

The paper formulates the multi-dimensional utility sequence mining problem and proposes two algorithms, MDUS_EM and MDUS_SD, to address it, enhancing pattern discovery in complex data.

Findings

Algorithms effectively discover useful patterns in multi-dimensional sequences.

Proposed methods outperform existing models in efficiency and insightfulness.

Experiments validate the framework's applicability to real-world datasets.

Abstract

Knowledge extraction from database is the fundamental task in database and data mining community, which has been applied to a wide range of real-world applications and situations. Different from the support-based mining models, the utility-oriented mining framework integrates the utility theory to provide more informative and useful patterns. Time-dependent sequence data is commonly seen in real life. Sequence data has been widely utilized in many applications, such as analyzing sequential user behavior on the Web, influence maximization, route planning, and targeted marketing. Unfortunately, all the existing algorithms lose sight of the fact that the processed data not only contain rich features (e.g., occur quantity, risk, profit, etc.), but also may be associated with multi-dimensional auxiliary information, e.g., transaction sequence can be associated with purchaser profile information. In this paper, we first formulate the problem of utility mining across multi-dimensional sequences, and propose a novel framework named MDUS to extract Multi-Dimensional Utility-oriented Sequential useful patterns. Two algorithms respectively named MDUS_EM and MDUS_SD are presented to address the formulated problem. The former algorithm is based on database transformation, and the later one performs pattern joins and a searching method to identify desired patterns across multi-dimensional sequences. Extensive experiments are carried on five real-life datasets and one synthetic dataset to show that the proposed algorithms can effectively and efficiently discover the useful knowledge from multi-dimensional sequential databases. Moreover, the MDUS framework can provide better insight, and it is more adaptable to real-life situations than the current existing models.