Association rule mining algorithms for set-valued data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2690, Issue , 2004, Pages 669-676

  Shoemaker, Christopher A ^a   Ruiz, Carolina ^a  

^a Worcester Polytechnic Institute   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ASSOCIATION RULES; BIOINFORMATICS; ENGINEERING EDUCATION;

DATA-MINING TOOLS; INPUT FILES; MINE ASSOCIATION RULES; MINING OF ASSOCIATION RULES; RULE MINING ALGORITHMS; SET-VALUED ATTRIBUTES; SET-VALUED DATUM; STANDARD TOOLS;

DATA MINING;

EID: 35048823856     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Article

References (16)

1. R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In Proc. 20th VLDB Conference, pages 487-499, 1994.
2. Rakesh Agrawal, Tomasz Imielinski, and Arun Swami. Mining association rules between sets of items in large databases. SIGMOD Records, 22(2):207-216, June 1993.
3. Liu Bing, Wynne Hsu, MaYiming, Wong Ching Kian, Hu Minqing, XiaYiyuan, and Liu Jing. Classification based on associations (CBA). http://www.comp.nus. edu.sg/~dm2/.
4. th AAAI Conf., 1996.
5. L. Cristofor and D. Cristofor. Association rules miner (ARMiner). http://www.cs.umb.edu/laur/ARMiner/.
6. D. Doyle, J. Judecki, J. Lund, and B. Padovano. Genomic data mining. Undergraduate Graduation Project (MQP). Worcester Polytechnic Institute, April 2001.
7. D. Kalles and A. Papagelis. Induction of decision trees in numeric domains using set-valued attributes, 2000.
8. W. Lin, S.A. Alvarez, and C. Ruiz. Efficient adaptive-support association rule mining for recommender systems. Data Mining and Knowledge Discovery, 6(1):83-105, Jan. 2002.
9. th KDD Conf., pages 80-86, New York, August 1998.
10. B. Murphy, D. Phu, I. Pushee, and F. Tan. Motif- and expression-based classification of DNA. Undergraduate Graduation Project (MQP). Worcester Polytechnic Institute, April 2001.
11. G. Novelli, S. Semprini, F. Capon, and B. Dallapiccola. A possible role of naip gene deletions in sex-related spinal muscular atrophy phenotype variation. Neurogenetics, 1(1):29-30, 1997.
12. Terry R. Payne. Instance-based prototypical learning of set valued attributes, 1995. URL=citeseer.nj.nec.com/payne95instancebased.html.
13. C. Shoemaker, M. Pungliya, M. Sao Pedro, C. Ruiz, S.A. Alvarez, M. Ward, E. Ryder, and J. Krushkal. Computational methods for single point and multipoint analysis of genetic variants associated with a simulated complex disorder in a general population. Genetic Epidemiology, 21 (Suppl. 1):S738-S745, 2001.
14. th Genetic Analysis Workshop, pages 449-453. Southwest Foundation for Biomedical Research, Oct. 2000.
15. B. Wirth, M. Herz, A. Wetter, S. Moskau, E. Hahnen, S. Rudnik-Schoencborn, T. Wienker, and K. Zerres. Quantitative analysis of survival motor neuron copies: Identification of subtle smnl mutations in patients with spinal muscular atrophy, genotype-phenotype correlation, and implications for genetic counseling. American Jounal of Human Genetics, 64:1340-1356, 1999.
16. Ian H. Witten and Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann Publishers, October 1999.