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首页> 外文期刊>ACM Transactions on Management Information Systems >Exploring Decomposition for Solving Pattern Mining Problems
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Exploring Decomposition for Solving Pattern Mining Problems

机译：探索解决模式挖掘问题的分解

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This article introduces a highly efficient pattern mining technique called Clustering-based Pattern Mining (CBPM). This technique discovers relevant patterns by studying the correlation between transactions in the transaction database based on clustering techniques. The set of transactions is first clustered, such that highly correlated transactions are grouped together. Next, we derive the relevant patterns by applying a pattern mining algorithm to each cluster. We present two different pattern mining algorithms, one applying an approximation-based strategy and another based on an exact strategy. The approximation-based strategy takes into account only the clusters, whereas the exact strategy takes into account both clusters and shared items between clusters. To boost the performance of the CBPM, a GPU-based implementation is investigated. To evaluate the CBPM framework, we perform extensive experiments on several pattern mining problems. The results from the experimental evaluation show that the CBPM provides a reduction in both the runtime and memory usage. Also, CBPM based on the approximate strategy provides good accuracy, demonstrating its effectiveness and feasibility. Our GPU implementation achieves significant speedup of up to 552× on a single GPU using big transaction databases.
机译：本文介绍了一种高效的模式挖掘技术，称为基于聚类的模式挖掘（CBPM）。该技术通过基于聚类技术研究事务数据库之间的交易之间的相关性来发现相关模式。这组交易是首先群集的，使得高度相关的事务被分组在一起。接下来，我们通过将模式挖掘算法应用于每个群集来派生相关模式。我们提出了两种不同的模式挖掘算法，一个基于精确的策略应用了基于近似的策略。基于近似的策略仅考虑了集群，而确切的策略考虑了集群之间的群集和共享项目。为了提高CBPM的性能，研究了基于GPU的实现。为了评估CBPM框架，我们对几个模式挖掘问题进行了广泛的实验。来自实验评估的结果表明，CBPM在运行时和内存使用情况下提供了减少。此外，基于近似策略的CBPM提供了良好的准确性，展示了其有效性和可行性。我们的GPU实现使用大事务数据库实现了单个GPU的显着加速度高达552倍。

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