数据流频繁项集（MFI）挖掘：基于流事务上下文变化分析（CVA）的可变窗口（VWS）
Mining Frequent Itemsets (MFI) over Data Streams: Variable Window Size (VWS) by Context Variation Analysis (CVA) of the Streaming Transactions
摘要：
针对数据流中频繁项挖掘所面临的窗口大小可变和存储空间不足的问题，提出了一种基于数据流的频繁项挖掘方法。为了检查流事务中上下文变化的变化点，我们开发了一种窗口结构，该窗口结构将在两个级别上，并支持即时固定窗口大小，控制异质性，并确保添加到窗口中的事务之间的同质性。为了最小化内存利用率、计算成本并提高进程可扩展性，该设计将允许在窗口级别固定覆盖或支持。在本文中，介绍了从窗口中增量挖掘频繁项集和上下文变化分析方法。我们在本文档中介绍的完整技术被命名为使用上下文变化分析固定的可变窗口大小的频繁项集挖掘（MFI-VWS-CVA）。在特定的项集中，频繁项集和非频繁项集之间有着明确的界限。在这个设计中，我们使用窗口大小的变化来表示信息流中的概念漂移。可以说，只要在有效地设置窗口大小方面存在问题，项集就会变得不频繁。我们已经执行和记录的实验证明，我们所设计的算法是比现有的效率更高。
英文摘要：
The challenges with respect to mining frequent items over data streaming engaging variable window size and low memory space are addressed in this research paper. To check the varying point of context change in streaming transaction we have developed a window structure which will be in two levels and supports in fixing the window size instantly and controls the heterogeneities and assures homogeneities among transactions added to the window. To minimize the memory utilization, computational cost and improve the process scalability, this design will allow fixing the coverage or support at window level. Here in this document, an incremental mining of frequent item-sets from the window and a context variation analysis approach are being introduced. The complete technology that we are presenting in this document is named as Mining Frequent Item-sets using Variable Window Size fixed by Context Variation Analysis (MFI-VWS-CVA). There are clear boundaries among frequent and infrequent item-sets in specific item-sets. In this design we have used window size change to represent the conceptual drift in an information stream. As it were, whenever there is a problem in setting window size effectively the item-set will be infrequent. The experiments that we have executed and documented proved that the algorithm that we have designed is much efficient than that of existing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
