SciCSM: novel contrast set mining over scientific datasets using bitmap indices
SciCSM: novel contrast set mining over scientific datasets using bitmap indices
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SciCSM：使用位图索引对科学数据集进行新颖的对比集挖掘
DOI：
10.1145/2791347.2791361
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
G. Agrawal
中科院分区：
文献类型：
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作者：
Gangyi Zhu;Yi Wang;G. Agrawal
对比集挖掘是一种广泛适用的探索性技术，它识别不同对照组之间有趣的差异。现有的算法主要针对具有分类属性的关系数据集。显然有必要应用这种方法来发现科学数据集中有趣的模式，这些数据集以数值数组为特征。在本文中，我们提出了一种新的算法--SciCSM，用于对基于数组的数据集进行高效的对比集挖掘。我们定义了如何为数字和数组数据描述“有趣的”对比集--处理子集可以同时涉及基于值和/或基于维的属性的事实。我们广泛使用位图索引来降低计算复杂性，并支持处理更大规模的数据。通过使用多个真实数据集，我们证明了算法的高效性和有效性。
Contrast set mining is a broadly applicable exploratory technique, which identifies interesting differences across contrast groups. The existing algorithms primarily target relational datasets with categorical attributes. There is clearly a need to apply this method to discover interesting patterns across scientific datasets, which feature arrays with numeric values. In this paper, we present a novel algorithm, SciCSM, for efficient contrast set mining over array-based datasets. We define how "interesting" contrast sets can be characterized for numeric and array data -- handling the fact that subsets can involve both value-based and/or dimension-based attributes. We extensively use bitmap indices to reduce computational complexity and enable processing of larger-scale data. We demonstrate both high efficiency and effectiveness of our algorithm by using multiple real-life datasets.