TY - JOUR
T1 - A new approach to generate frequent patterns from enterprise databases
AU - Liu, Yu Chin
AU - Hsu, Ping Yu
PY - 2005
Y1 - 2005
N2 - As data mining techniques are explored extensively, incorporating discovered knowledge into business leads to superior competitive advantages. Most techniques in mining association rules nowadays are designed to solve problems based on de-normalized transaction files. Namely, normalized transaction tables should be transformed before mining methods could be applied, and some previous works have pointed that such data transformation usually consumes a lot of resources. As a result, this study proposes a new method which incorporates mining algorithms with enterprise transaction databases directly. In addition, in most well-known mining algorithms, the minimum support threshold is used in deciding whether the pattern is frequent or not, and it is crucial to define an appropriate threshold before performing mining tasks. Since setting an appropriate threshold cannot be done intuitively by domain experts or users, they usually set the threshold through trial and error. Usually, while setting different minimum support thresholds, most existing algorithms re-perform all mining procedures. Consequently, it takes a lot of computations. Our new method explores such circumstances and provides ways to flexibly adjust support thresholds without re-doing the whole mining task.
AB - As data mining techniques are explored extensively, incorporating discovered knowledge into business leads to superior competitive advantages. Most techniques in mining association rules nowadays are designed to solve problems based on de-normalized transaction files. Namely, normalized transaction tables should be transformed before mining methods could be applied, and some previous works have pointed that such data transformation usually consumes a lot of resources. As a result, this study proposes a new method which incorporates mining algorithms with enterprise transaction databases directly. In addition, in most well-known mining algorithms, the minimum support threshold is used in deciding whether the pattern is frequent or not, and it is crucial to define an appropriate threshold before performing mining tasks. Since setting an appropriate threshold cannot be done intuitively by domain experts or users, they usually set the threshold through trial and error. Usually, while setting different minimum support thresholds, most existing algorithms re-perform all mining procedures. Consequently, it takes a lot of computations. Our new method explores such circumstances and provides ways to flexibly adjust support thresholds without re-doing the whole mining task.
UR - http://www.scopus.com/inward/record.url?scp=27244437129&partnerID=8YFLogxK
U2 - 10.1007/11551188_40
DO - 10.1007/11551188_40
M3 - 會議論文
AN - SCOPUS:27244437129
SN - 0302-9743
VL - 3686
SP - 371
EP - 380
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
IS - PART I
T2 - Third International Conference on Advances in Patten Recognition, ICAPR 2005
Y2 - 22 August 2005 through 25 August 2005
ER -