On discovering "potentially useful" patterns from databases

被引：0

作者：

Xie, Ying ^{[1
]}

Johnsten, Tom ^{[2
]}

Raghavan, Vijay V. ^{[3
]}

Ramachandran, K. ^{[3
]}

机构：

[1] Kennesaw State Univ, Dept Comp Sci & Informat Syst, 1000 Chastain Rd, Kennesaw, GA 30144 USA

[2] Univ South Alabam, Sch Informat & Comp Sci, Mobile, AL USA

[3] Univ Louisian, Ctr Adv & Comp Studies, Lafayette, LA USA

来源：

2006 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING | 2006年

关键词：

potentially useful patterns; KDD; data mining; logic foundation; DAPUP;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

As is generally accepted, the most important feature that a KDD system must possess is the ability to discover patterns that are "potentially useful". In order to allow KDD systems to make potentially useful judgments, we give formal definitions of "potential usefulness" by completely staying within the realms of the expressiveness provided by Bacchus' Probabilistic Logic Language. Furthermore, a tractable algorithm is proposed that is capable of discovering all potentially useful patterns from databases, given limited accessible information.

引用

页码：494 / +

页数：2

共 50 条

[1] Discovering useful useful patterns from multiple instance data
Luna, J. M.
Cano, A.
Sakalauskas, V.
Ventura, S.
[J]. INFORMATION SCIENCES, 2016, 357 : 23 - 38
[2] Discovering consensus patterns in biological databases
ElTabakh, Mohamed Y.
Aref, Walid G.
Ouzzani, Mourad
Ali, Mohamed H.
[J]. DATA MINING AND BIOINFORMATICS, 2006, 4316 : 170 - +
[3] Fast algorithm to discovering sequential patterns from large databases
Hu Huirong
[J]. PROCEEDINGS OF THE 24TH CHINESE CONTROL CONFERENCE, VOLS 1 AND 2, 2005, : 1352 - 1355
[4] Discovering relational patterns across multiple databases
Zhu, Xingquan
Wu, Xindong
[J]. 2007 IEEE 23RD INTERNATIONAL CONFERENCE ON DATA ENGINEERING, VOLS 1-3, 2007, : 701 - +
[5] Discovering useful and understandable patterns in manufacturing data
Last, M
Kandel, A
[J]. ROBOTICS AND AUTONOMOUS SYSTEMS, 2004, 49 (3-4) : 137 - 152
[6] An efficient tool for discovering simple combinatorial patterns from large text databases
Arimura, H
Wataki, A
Fujino, R
Shimozono, S
Arikawa, S
[J]. DISCOVERY SCIENCE, 1998, 1532 : 393 - 394
[7] Discovering sequential patterns from non-uniform databases (extended abstract)
Dang, D
Wang, XS
[J]. WEB-AGE INFORMATION MANAGEMENT, PROCEEDINGS, 2000, 1846 : 139 - 144
[8] Discovering frequent structured patterns from string databases: An application to biological sequences
Palopoli, L
Terracina, G
[J]. DISCOVERY SCIENCE, PROCEEDINGS, 2002, 2534 : 34 - 46
[9] Negative Itemset Tree for Discovering Rare Patterns with Periodicity from Static Databases
Jyothi Upadhya, K.
Bolisetty, Thanishka
Rao, B. Dinesh
Geetha, M.
[J]. Engineering Letters, 2023, 31 (03) : 882 - 895
[10] Discovering Partial Periodic Spatial Patterns in Spatiotemporal Databases
Kiran, R. Uday
Saideep, C.
Zettsu, Koji
Toyoda, Masashi
Kitsuregawa, Masaru
Reddy, P. Krishna
[J]. 2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2019, : 233 - 238

← 1 2 3 4 5 →