| dc.description.abstract |
Associationship is an important component of data mining. In real world, the knowledge
used for mining rule is almost time varying. The items have the dynamic characteristic in
terms of transaction, which have seasonal selling rate and it holds time-based
associationship with another item. In database, some items which are infrequent in whole
dataset may be frequent in a particular time period. If these items are ignored then
associationship result will no longer be accurate. To restrict the time based associationship,
calendar based pattern can be used [5]. Calendar units 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.[11] Our focus is
to find effective time sensitive algorithm for mining associationship by extending frequent
pattern tree approach [3]. This algorithm reduces the time complexity of existing technique[5].
It also uses the advantages of divide & conquer method to decompose the mining task into
a smaller tasks for database. |
en_US |