Title :
Mining association rules from relations on a parallel NCR teradata database system
Author :
Chung, Soon M. ; Mangamuri, Murali
Author_Institution :
Dept. of Comput. Sci. & Eng., Wright State Univ., Dayton, OH, USA
Abstract :
Data mining from relations is becoming increasingly important with the advent of parallel database systems. In this paper, we propose a new algorithm for mining association rules from relations. The new algorithm is an enhanced version of the SETM algorithm of M. Houtsma and A. Swami (1995), and it reduces the number of candidate itemsets considerably. We implemented and evaluated the new algorithm on a parallel NCR teradata database system. The new algorithm is much faster than the SETM algorithm, and its performance is quite scalable.
Keywords :
data mining; parallel databases; relational databases; very large databases; SETM algorithm; mining association rules; parallel NCR teradata database system; relational data mining; Algorithm design and analysis; Association rules; Computer science; Data engineering; Data mining; Database systems; Itemsets; Performance analysis; Relational databases; Transaction databases;
Conference_Titel :
Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004. International Conference on
Print_ISBN :
0-7695-2108-8
DOI :
10.1109/ITCC.2004.1286500
