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Mining and applications of repeating patterns

机译:重复模式的挖掘和应用

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摘要

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-pattern-based 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.
机译:摘要挖掘真实数据的宝贵知识已经很长时间是一个热门话题。重复模式是在许多真实应用中发生的重要知识之一,例如音乐数据和医疗数据。在本文中,我们的目的是贡献一种用于重复模式的有效采矿算法,并使用所开采的重复模式进行真实应用。在挖掘重复模式方面,虽然在这个问题上进行了许多过去的研究,但表现仍无法赢得用户的满意度,特别是对于大数据集。对于此问题,在本文中,我们提出了一种名为重复模式的快速挖掘的有效算法,这实现了通过称为Quick-Pattern索引的新颖索引来发现重复模式的高性能。在应用方面,提出了一种名为基于模式的音乐推荐系统的音乐推荐系统,以处理音乐推荐中的问题。甚至面临非常稀疏的评级矩阵,建议仍然可以完成。实验结果表明,我们所提出的采矿算法和推荐系统分别在效率和有效性方面优于先前的工作。

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