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An Efficient Incremental Mining Algorithm for Dynamic Databases

机译：动态数据库的高效增量挖掘算法

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Data mining is aimed to extract hidden acknowledge from large dataset, in order to exploit it for predicting future trends and make decisions. Extracting meaningful and useful candidate optimally is handled by several algorithms, mainly those based on exploring incoming data, which can lose information. To address this issue, this paper proposes an algorithm named Incremental Apriori (IncA) for discovering frequent itemsets in transaction databases, which is in fact a variant of the well-known Apriori algorithm. In IncA, we introduce a notion of promising items generated from the original database, an incremental technique applied on incremental database and a health check process to ensure candidate generation completeness. On the theoretical side, our algorithm exhibits the best computational complexity compared to the recent state-of-the-art algorithms. On the other hand, we tested the proposed approach on large synthetic databases. The obtained results prove that IncA reduces the running time as well as the search space and also show that our algorithm performs better than the Apriori algorithm.
机译：数据挖掘旨在从大型数据集中提取隐藏的确认，以便将其用于预测未来趋势并做出决策。最佳地提取有意义且有用的候选项由几种算法处理，主要是基于探索输入数据的算法，这些算法可能会丢失信息。为了解决这个问题，本文提出了一种名为Incremental Apriori（IncA）的算法，用于发现交易数据库中的频繁项目集，它实际上是众所周知的Apriori算法的一种变体。在IncA中，我们引入了从原始数据库生成的有前途的项目的概念，应用于增量数据库的增量技术以及运行状况检查过程以确保候选者生成的完整性。从理论上讲，与最新技术相比，我们的算法具有最佳的计算复杂性。另一方面，我们在大型综合数据库上测试了建议的方法。获得的结果证明IncA减少了运行时间并减少了搜索空间，也表明我们的算法性能优于Apriori算法。

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