The discovery of association rules in the rapid flow of data using a sliding chute window

Abstract. Window method based on the decline all set items are stored alphabetically in a tree. Each node in the tree represents a set of pens. In addition, an entry for each node keeps his collection of relevant data item. Cnt value reflects the current iteration of the model, blurred and tid specifies the number of the last transaction that contains the item has been set. Since the data distribution may change over the data flow, the sliding window method based on Only recently have shifted their attention to the data observed. Some data sets may be part of the data stream, but may be very new data entry and increasing data volume of data in another part of the same set of data with very low or even zero. For Fixing these algorithms have been introduced based on sliding windows that are turning their attention to the part of the data and the data prior to the most recent data not be exposed to fewer repetitions gradually so that the non-items become and the list of data that are evaluated in each window are deleted.