Most pattern mining techniques almost singularly focus on identifying frequent patterns and very less attention has been paid to the generation of rare patterns. However, in several domains, recognizing less frequent but strongly related patterns have greater advantage over the former ones. Identification of compelling and meaningful rare associations among such patterns may proved to be significant for air quality management that has become an indispensable task in today’s world. The rare correlations between air pollutants and other parameters may aid in restricting the air pollution to a manageable level. To this end, efficient and competent rare pattern mining techniques are needed that can generate the complete set of rare patterns, further identifying significant rare association rules among them. Moreover, a notable issue with databases is their continuous update over time due to the addition of new records. The users requirement or behavior may change with the incremental update of databases that makes it difficult to determine a suitable support threshold for the extraction of interesting rare association rules. This paper, presents an efficient rare pattern mining technique to capture the complete set of rare patterns from a real environmental dataset. The proposed approach does not restart the entire mining process upon threshold update and generates the complete set of rare association rules in a single database scan. It can effectively perform incremental mining and also provides flexibility to the user to regulate the value of support threshold for generating the rare patterns. Significant rare association rules representing correlations between air pollutants and other environmental parameters are further extracted from the generated rare patterns to identify the substantial causes of air pollution. Performance analysis shows that the proposed method is more efficient than existing rare pattern mining approaches in providing significant directions to the domain experts for air pollution monitoring.

中文翻译：

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一种挖掘稀有关联规则的有效方法：以空气污染为例

大多数模式挖掘技术几乎都专注于识别频繁模式，而很少关注稀有模式的生成。然而，在几个领域中，识别频率较低但相关性强的模式比前者具有更大的优势。确定这些模式之间令人信服且有意义的罕见关联可能被证明对空气质量管理具有重要意义，而空气质量管理已成为当今世界不可或缺的任务。空气污染物与其他参数之间罕见的相关性可能有助于将空气污染限制在可管理的水平。为此，需要高效且称职的稀有模式挖掘技术，以生成完整的稀有模式集，进一步识别其中重要的稀有关联规则。而且，数据库的一个显着问题是由于添加了新记录，它们会随着时间的推移不断更新。用户的需求或行为可能会随着数据库的增量更新而发生变化，这使得难以确定合适的支持阈值以提取有趣的稀有关联规则。本文提出了一种有效的稀有模式挖掘技术，用于从真实环境数据集中捕获完整的稀有模式集。所提出的方法不会在阈值更新时重新启动整个挖掘过程，并在单个数据库扫描中生成完整的稀有关联规则集。它可以有效地进行增量挖掘，也为用户提供了灵活性来调节支持阈值的值以生成稀有模式。从生成的稀有模式中进一步提取表示空气污染物与其他环境参数之间相关性的重要稀有关联规则，以识别空气污染的实质原因。性能分析表明，所提出的方法比现有的稀有模式挖掘方法更有效地为领域专家提供空气污染监测的重要方向。