Targeted Mining Precise-positioning Episode Rules

TL;DR

This paper introduces TaMIPER, an algorithm for targeted mining of precise-positioning episode rules, significantly improving efficiency and accuracy for user-specified event sequences in various data mining applications.

Contribution

The paper defines targeted episode rules and develops the TaMIPER algorithm with strategies to enhance mining efficiency and accuracy for user-defined episodes.

Findings

TaMIPER reduces time and space complexity.

High accuracy in mining user-interest episode rules.

Effective in diverse domains like weather, security, and e-commerce.

Abstract

The era characterized by an exponential increase in data has led to the widespread adoption of data intelligence as a crucial task. Within the field of data mining, frequent episode mining has emerged as an effective tool for extracting valuable and essential information from event sequences. Various algorithms have been developed to discover frequent episodes and subsequently derive episode rules using the frequency function and anti-monotonicity principles. However, currently, there is a lack of algorithms specifically designed for mining episode rules that encompass user-specified query episodes. To address this challenge and enable the mining of target episode rules, we introduce the definition of targeted precise-positioning episode rules and formulate the problem of targeted mining precise-positioning episode rules. Most importantly, we develop an algorithm called Targeted Mining Precision Episode Rules (TaMIPER) to address the problem and optimize it using four proposed strategies, leading to significant reductions in both time and space resource requirements. As a result, TaMIPER offers high accuracy and efficiency in mining episode rules of user interest and holds promising potential for prediction tasks in various domains, such as weather observation, network intrusion, and e-commerce. Experimental results on six real datasets demonstrate the exceptional performance of TaMIPER.