Discovering Spatial Co-location Patterns: A Summary of Results
Discovering Spatial Co-location Patterns: A Summary of Results
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DOI：
10.1007/3-540-47724-1_13
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发表时间：
2001-07
期刊：
影响因子：
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通讯作者：
S. Shekhar;Y. Huang
中科院分区：
文献类型：
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作者：
S. Shekhar;Y. Huang
给定布尔空间特征的集合，共置模式发现过程会找到经常位于一起的特征子集。例如，生态数据集的分析可以揭示火点火源特征与针叶植被类型特征和干旱特征的频繁共置。空间共置规则问题与关联规则问题不同。尽管布尔空间特征类型（也称为空间事件）可能对应于市场篮数据集的关联规则中的项目，但不存在交易的自然概念。这给使用传统度量（例如支持度、置信度）和应用使用基于支持度的剪枝的关联规则挖掘算法带来了困难。我们提出了用户指定邻域的概念来代替交易来指定项目组。提出了针对空间共置模式的新兴趣度量，该度量在面对潜在的无限重叠邻域时是稳健的。我们还提出了一种算法来挖掘频繁的空间共置模式并分析其正确性和完整性。我们计划在不久的将来进行实验评估和性能调优。
Given a collection of boolean spatial features, the co-location pattern discovery process finds the subsets of features frequently located together. For example, the analysis of an ecology dataset may reveal the frequent co-location of a fire ignition source feature with a needle vegetation type feature and a drought feature. The spatial co-location rule problem is different from the association rule problem. Even though boolean spatial feature types (also called spatial events) may correspond to items in association rules over market-basket datasets, there is no natural notion of transactions. This creates difficulty in using traditional measures (e.g. support, confidence) and applying association rule mining algorithms which use support based pruning. We propose a notion of user-specified neighborhoods in place of transactions to specify groups of items. New interest measures for spatial co-location patterns are proposed which are robust in the face of potentially infinite overlapping neighborhoods. We also propose an algorithm to mine frequent spatial co-location patterns and analyze its correctness, and completeness. We plan to carry out experimental evaluations and performance tuning in the near future.