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首页> 外文会议>FTRA international conference on computer science and its applications >Mining Frequent Graph Patterns Considering Both Different Importance and Rarity of Graph Elements
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Mining Frequent Graph Patterns Considering Both Different Importance and Rarity of Graph Elements

机译：同时考虑图形元素的重要性和稀疏性的频繁图形模式的挖掘

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摘要

Since frequent graph pattern mining was proposed, various approaches have been suggested by devising efficient techniques or integrating graph mining with other mining areas. However, previous methods have limitations that cannot reflect the following important characteristics in the real world to their mining processes. First, elements in the real world have their own importance as well as frequency, but traditional graph mining methods do not consider such features. Second, various elements composing graph databases may need thresholds different from one another according to their characteristics. However, since traditional approaches mine graph patterns on the basis of only a single threshold, losses of important pattern information can be caused. Motivated by these problems, we propose a new graph mining algorithm that can consider both different importance and multiple thresholds for each element of graphs. We also demonstrate outstanding performance of the proposed algorithm by comparing ours with previous state-of-the-art approaches.
机译：由于提出了频繁的图形模式挖掘，因此通过设计有效的技术或将图挖掘与其他采矿区域集成提出了各种方法。但是，先前的方法具有局限性，无法将现实世界中的以下重要特征反映到其采矿过程中。首先，现实世界中的元素具有自己的重要性和频率，但是传统的图挖掘方法没有考虑这些特征。其次，组成图形数据库的各种元素可能需要根据其特性彼此不同的阈值。但是，由于传统方法仅基于单个阈值来挖掘图形模式，因此可能导致重要模式信息的丢失。受这些问题的影响，我们提出了一种新的图挖掘算法，该算法可以针对图的每个元素同时考虑不同的重要性和多个阈值。通过将我们的算法与以前的最新方法进行比较，我们还证明了所提出算法的出色性能。

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