Efficient calendar based temporal association rule

摘要

Associationship is an important component of data mining. In real world data the knowledge used for mining rule is almost time varying. The item have the dynamic characteristic in terms of transaction, which have seasonal selling rate and it hold time-based associationship with another item. It is also important that in database, some items which are infrequent in whole dataset but those may be frequent in a particular time period. If these items are ignored then associationship WVW200R3100221-398 result will no longer be accurate. To restrict the time based associationship calendar based pattern can be used [YPXS03]. A calendar unit such as months and days, clock units, such as hours and seconds & specialized units , such as business days and academic years, play a major role in a wide range of information system applications[BXOO]. Most of the popular associationship rule mining methods are having performance bottleneck for database with different characteristics. Some of the methods are efficient for sparse dataset where as some are good for a dense dataset. Our focus is to find effective time sensitive algorithm using H-struct called temporal H-mine, which takes the advantage of this data structure and dynamically adjusts links in the mining process [PHNTY01]. It is faster in traversing & advantage of precisely predictable spaces overhead. It can be scaled up to large database by database partitioning, end when dataset becomes dense, conditionally temporal FP-tree. can be constructed dynamically as part of mining.