Mining and applications of repeating patterns

Abstract

Mining the valuable knowledge from real data has been a hot topic for a long time. Repeating pattern is one of the important knowledge, occurring in many real applications such as musical data and medical data. In this paper, our purposes are to contribute an efficient mining algorithm for repeating patterns and to conduct a real application using the repeating patterns mined. In terms of mining the repeating patterns, although a number of past studies were made on this issue, the performance cannot still earn the users' satisfactions especially for large data sets. For this issue, in this paper, we propose an efficient algorithm named Fast Mining of Repeating Patterns, which achieves high performance of discovering the repeating patterns by a novel index called Quick-Pattern Index. In terms of applications, a music recommender system named repeating-patternbased music recommender system is proposed to deal with problems in music recommendation. Even facing a very sparse rating matrix, the recommendation can still be completed. The experimental results show that our proposed mining algorithm and recommender system outperform the previous works in terms of efficiency and effectiveness, respectively.

On a New Algorithm for Removing Repeating Patterns in Similarity Analysis

“…The string-join operation finds the longest repeating pattern by iteratively joining short repeating patterns. Su, Hong, Chin, Liao, and Cheng (2018) argued that the generation of repeating patterns is inefficient because the cost of checking is high. Also, working with music data, Su et al (2018) proposed an algorithm that scans the sequence of a string once, which does not require storage of a matrix like Hsu et al’s.…”

“…Su, Hong, Chin, Liao, and Cheng (2018) argued that the generation of repeating patterns is inefficient because the cost of checking is high. Also, working with music data, Su et al (2018) proposed an algorithm that scans the sequence of a string once, which does not require storage of a matrix like Hsu et al’s. Their algorithm, however, requires a preassembled list of all possible patterns, and they did not describe how to construct such a list.…”