Flexible Pattern Discovery and Analysis

• URL: http://arxiv.org/abs/2111.12218v1
• Date: Wed, 24 Nov 2021 01:25:15 GMT
• Title: Flexible Pattern Discovery and Analysis
• Authors: Chien-Ming Chen, Lili Chen, and Wensheng Gan
• Abstract summary: We introduce an algorithm for the mining of flexible high utility-occupancy patterns. The proposed algorithm can effectively control the length of the derived patterns, for both real-world and synthetic datasets.
• Score: 2.075126998649103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Based on the analysis of the proportion of utility in the supporting transactions used in the field of data mining, high utility-occupancy pattern mining (HUOPM) has recently attracted widespread attention. Unlike high-utility pattern mining (HUPM), which involves the enumeration of high-utility (e.g., profitable) patterns, HUOPM aims to find patterns representing a collection of existing transactions. In practical applications, however, not all patterns are used or valuable. For example, a pattern might contain too many items, that is, the pattern might be too specific and therefore lack value for users in real life. To achieve qualified patterns with a flexible length, we constrain the minimum and maximum lengths during the mining process and introduce a novel algorithm for the mining of flexible high utility-occupancy patterns. Our algorithm is referred to as HUOPM+. To ensure the flexibility of the patterns and tighten the upper bound of the utility-occupancy, a strategy called the length upper-bound (LUB) is presented to prune the search space. In addition, a utility-occupancy nested list (UO-nlist) and a frequency-utility-occupancy table (FUO-table) are employed to avoid multiple scans of the database. Evaluation results of the subsequent experiments confirm that the proposed algorithm can effectively control the length of the derived patterns, for both real-world and synthetic datasets. Moreover, it can decrease the execution time and memory consumption.

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