School of Computer Engineering, Jiangsu Ocean University, Lianyungang, Jiangsu, China.
Lianyungang Huajie Senior High School, Lianyungang, Jiangsu, China.
Comput Intell Neurosci. 2022 Oct 14;2022:7430106. doi: 10.1155/2022/7430106. eCollection 2022.
With the increase of real-time stream data, knowledge discovery from stream data becomes more and more important, which requires an efficient data structure to store transactions and scan sliding windows once to discover frequent itemsets. We present a new method named Linking Compact Tree (LCTree). We designed an algorithm by using an improved data structure to create objective tree, which can find frequent itemsets with linear complexity. Secondly, we can merge items in sliding windows by one scan with Head Linking List data structure. Third, by implementing data structure of Tail Linking List, we can locate the obsolete nodes and remove them easily. Finally, LCTree is able to find all exact frequent items in data stream with reduced time and space complexity by using such a linear data structure. Experiments on datasets with different sizes and types were conducted to compare the proposed LCTree technique with well-known frequent item mining methods including Cantree, FP-tree, DSTree, CPSTree, and Gtree. The results of experiments show presented algorithm has better performance than other methods, and also confirm that it is a promising solution for detecting frequent item sets in real time applications.
随着实时流数据的增加，从流数据中发现知识变得越来越重要，这需要一种有效的数据结构来存储事务，并在一次扫描中扫描滑动窗口以发现频繁项集。我们提出了一种名为链接紧凑树（LCTree）的新方法。我们设计了一种算法，使用改进的数据结构创建目标树，可以以线性复杂度找到频繁项集。其次，我们可以使用 Head Linking List 数据结构在一次扫描中合并滑动窗口中的项。第三，通过实现 Tail Linking List 的数据结构，我们可以轻松定位过时的节点并将其删除。最后，LCTree 通过使用这种线性数据结构，能够以减少的时间和空间复杂度在数据流中找到所有精确的频繁项。我们在不同大小和类型的数据集上进行了实验，将提出的 LCTree 技术与知名的频繁项挖掘方法（包括 Cantree、FP-tree、DSTree、CPSTree 和 Gtree）进行了比较。实验结果表明，所提出的算法具有比其他方法更好的性能，也证实了它是实时应用中检测频繁项集的一种很有前途的解决方案。
