HARDWARE ENHANCED ASSOCIATION RULE MINING WITH HASHING AND PIPELINING - KNOWLEDGE AND DATA ENGINEERING
Data mining techniques have been widely used in various applications. One of the most important data mining applications is association rule mining.
Apriori-based association rule mining in hardware, one has to load candidate item sets and a database into the hardware.
Since the capacity of the hardware architecture is fixed, if the number of candidate item sets or the number of items in the database is larger than the hardware capacity, the items are loaded into the hardware separately.
The time complexity of those steps that need to load candidate item sets or database items into the hardware is in proportion to the number of candidate item sets multiplied by the number of items in the database. Too many candidate item sets and a large database would create a performance bottleneck.
In this paper, we propose a HAsh-based and PiPelIned (abbreviated as HAPPI) architecture for hardware enhanced association rule mining. Therefore, we can effectively reduce the frequency of loading the database into the hardware.
HAPPI solves the bottleneck problem in a priori-based hardware schemes.
Technology to use:.NET