Abstract
Carma is a type of online association algorithm, designed to facilitate association rule with online data flow and successively changing support thresholds. In this paper we study the factors that contribute to the efficiency of Carma and how data flow distribution give effects on the performance of Carma. We design several experiments with two kinds of data. In fixed support threshold situations, we compare Carma with that of Apriori. We find the sets generated by Carma are subsets of those generated by Apriori. We find that if the support threshold is reasonably defined, these two algorithms reach the same results. On the other hand, as the support threshold increases, Phase I generates less items and the number of deleted sets from Phase II first increases and then declines. Carma behaves consistently towards changing support. We notice the earlier the items enter into a lattice, the more accurate the estimations are. If base stone elements show up early in the transaction, the performance of Phase II is mainly influenced by the late-entered item sets. Based on the discussion with Carma, we propose a new procedure to improve Carma. Simulations reveal that the modified algorithm works well.
| Original language | English |
|---|---|
| Title of host publication | NCM 2009 - 5th International Joint Conference on INC, IMS, and IDC |
| Pages | 589-594 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2009 |
| Externally published | Yes |
| Event | NCM 2009 - 5th International Joint Conference on Int. Conf. on Networked Computing, Int. Conf. on Advanced Information Management and Service, and Int. Conf. on Digital Content, Multimedia Technology and its Applications - Seoul, Korea, Republic of Duration: Aug 25 2009 → Aug 27 2009 |
Other
| Other | NCM 2009 - 5th International Joint Conference on Int. Conf. on Networked Computing, Int. Conf. on Advanced Information Management and Service, and Int. Conf. on Digital Content, Multimedia Technology and its Applications |
|---|---|
| Country | Korea, Republic of |
| City | Seoul |
| Period | 8/25/09 → 8/27/09 |
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Keywords
- Apriori
- Association rule algorithm
- Carma
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Computer Science Applications
- Software
Cite this
Efficiency and consistency study on Carma. / Huang, Yuan; Wang, Xing; Shia, Ben Chang.
NCM 2009 - 5th International Joint Conference on INC, IMS, and IDC. 2009. p. 589-594 5331786.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Efficiency and consistency study on Carma
AU - Huang, Yuan
AU - Wang, Xing
AU - Shia, Ben Chang
PY - 2009
Y1 - 2009
N2 - Carma is a type of online association algorithm, designed to facilitate association rule with online data flow and successively changing support thresholds. In this paper we study the factors that contribute to the efficiency of Carma and how data flow distribution give effects on the performance of Carma. We design several experiments with two kinds of data. In fixed support threshold situations, we compare Carma with that of Apriori. We find the sets generated by Carma are subsets of those generated by Apriori. We find that if the support threshold is reasonably defined, these two algorithms reach the same results. On the other hand, as the support threshold increases, Phase I generates less items and the number of deleted sets from Phase II first increases and then declines. Carma behaves consistently towards changing support. We notice the earlier the items enter into a lattice, the more accurate the estimations are. If base stone elements show up early in the transaction, the performance of Phase II is mainly influenced by the late-entered item sets. Based on the discussion with Carma, we propose a new procedure to improve Carma. Simulations reveal that the modified algorithm works well.
AB - Carma is a type of online association algorithm, designed to facilitate association rule with online data flow and successively changing support thresholds. In this paper we study the factors that contribute to the efficiency of Carma and how data flow distribution give effects on the performance of Carma. We design several experiments with two kinds of data. In fixed support threshold situations, we compare Carma with that of Apriori. We find the sets generated by Carma are subsets of those generated by Apriori. We find that if the support threshold is reasonably defined, these two algorithms reach the same results. On the other hand, as the support threshold increases, Phase I generates less items and the number of deleted sets from Phase II first increases and then declines. Carma behaves consistently towards changing support. We notice the earlier the items enter into a lattice, the more accurate the estimations are. If base stone elements show up early in the transaction, the performance of Phase II is mainly influenced by the late-entered item sets. Based on the discussion with Carma, we propose a new procedure to improve Carma. Simulations reveal that the modified algorithm works well.
KW - Apriori
KW - Association rule algorithm
KW - Carma
UR - http://www.scopus.com/inward/record.url?scp=73549110648&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=73549110648&partnerID=8YFLogxK
U2 - 10.1109/NCM.2009.241
DO - 10.1109/NCM.2009.241
M3 - Conference contribution
AN - SCOPUS:73549110648
SN - 9780769537696
SP - 589
EP - 594
BT - NCM 2009 - 5th International Joint Conference on INC, IMS, and IDC
ER -