An Algorithm for Interesting Negated Itemsets for Negative Association Rules from XML Stream Data

This Upcoming IoT revolution changes completely around the world transforming everyday physical objects into innovation technologies. With the advances of sensor network, billions of objects communicate wirelessly, which generates gigantic volumes of stream data. It highly demands knowledge discovery methods to extract valuable information. Association rules mining is one of well-known methods to analyze, manage, and leverage huge volumes of data. Researchers are devoted to elaborating solutions for the stream data. However, they are faced with several problems, such as real-time processing, complex data structure, heterogeneous environments, and so on. In this paper, we consider discovery of meaningful but negated tree-structured itemsets for negative association rules from the famous tree-structured data type, XML, which is not just xml but streaming xml data. We provide a frame of algorithm with several measuring factors.