Pair Triplet Association Rule Generation in Streams Pair Triplet Association Rule Generation in Streams Pair Triplet Association Rule Generation in Streams Pair Triplet Association Rule Generation in Streams

Many applications involve the generation and analysis of a new kind of data, called stream data, where data flows in and out of an observation platform or window dynamically. Such data streams have the unique features such as huge or possibly infinite volume, dynamically changing, flowing in or out in a fixed order, allowing only one or a small number of scans. An important problem in data stream mining is that of finding frequent items in the stream. This problem finds application across several domains such as financial systems, web traffic monitoring, internet advertising, retail and e-business. This raises new issues that need to be considered when developing association rule mining technique for stream data. The SpaceSaving algorithm reports both frequent and top-k elements with tight guarantees on errors. We also develop the notion of association rules in streams of elements. The Streaming-Rules algorithm is integrated with Space-Saving algorithm to report 11 association rules with tight guarantees on errors, using minimal space, and limited processing per element and we are using Apriori algorithm for static datasets and generation of association rules and implement Streaming-Rules algorithm for pair, triplet association rules. We compare the top- rules of static datasets with output of stream datasets and find percentage of error.