Constraining and summarizing association rules in medical data

Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. ACM SIGMOD conference, pp 207–216

Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. VLDB conference, pp 487–499

Bastide Y, Pasquier N, Taouil R, Lakhal GL (2000) Mining minimal non-redundant association rules using frequent closed itemsets. Computational logic, pp 972–986

Bayardo R, Agrawal R (1999) Mining the most interesting rules. ACM KDD conference, pp 145–154

Becquet C, Blachon S, Jeudy B, Boulicaut J, Gandrillon O (2002) Strong association-rule mining for large-scale gene-expression data analysis: a case study on human SAGE data. Genom Biol 3(12)

Braal L, Ezquerra N, Schwartz E, Garcia EV (1996) Analyzing and predicting images through a neural network approach. In: Proceedings of visualization in biomedical computing, pp 253–258

Brin S, Motwani R, Ullman J, Tsur S (1997) Dynamic itemset counting and implication rules for market basket data. ACM SIGMOD conference, pp 255–264

Brossette S, Sprague A, Hardin J, Waites K, Jones W, Moser S (1998) Association rules and data mining in hospital infection control and public health surveillance. J Am Med Inform Assoc (JAMIA) 5(4):373–381

Brossette S, Sprague A, Jones W, Moser S (2000) A data mining system for infection control surveillance. Methods Inf Med 39(4):303–310

Bykowski A, Rigotti C (2003) Dbc: a condensed representation of frequent patterns for efficient mining. Inform Syst 28(8):949–977

Chen T, Chou L, Hwang S (2003) Application of a data mining technique to analyze coprescription patterns for antacids in Taiwan. Clin Ther 25(9):2453–2463

Cooke D, Ordonez C, Garcia E.V, Omiecinski E, Krawczynska E, Folks R, Santana C, de Braal L, Ezquerra N (1999) Data mining of large myocardial perfusion SPECT (MPS) databases to improve diagnostic decision making. J Nuclear Med 40(5)

Cooke D, Santana C, Morris T, de Braal L, Ordonez C, Omiecinski E, Ezquerra N, Garcia E (2000a) Data mining of large myocardial perfusion SPECT (MPS) databases: validation of expert system rule confidences. J Nuclear Med 41(5):187

Cooke D, Santana C, Morris T, de Braal L, Ordonez C, Omiecinski E, Ezquerra N, Garcia EV (2000b) Validating expert system rule confidences using data mining of myocardial perfusion SPECT databases. Computers in cardiology conference, pp 116–119

Creighton C, Hanash S (2003) Mining gene expression databases for association rules. Bioinformatics 19(1):79–86

Cristofor L, Simovici D (2002) Generating an informative cover for association rules. ICDM, pp 597–600

Delgado M, Sanchez D, Martin-Bautista M, Vila M (2001) Mining association rules with improved semantics in medical databases. Artif Intell Med 21(1–3):241–245

Down S, Wallace M (2000) Mining association rules from a pediatric primary care decision support system. In: Proceedings of AMIA symposium, pp 200–204

Ezquerra N, Mullick R (1993) Perfex: an expert system for interpreting myocardial perfusion. Expert Syst Appl 6:455–468

Fraser H, Long W, Naimi S (2003) Evaluation of a cardiac diagnostic program in a typical clinical setting. J Am Med Inform Assoc (JAMIA) 10(4):373–381

Freitas A (2000) Understanding the crucial differences between classification and association rules–-a position paper. SIGKDD Explor 2(1):65–69

Gade K, Wan J, Karypis G (2004) Efficient closed pattern mining in the presence of tough block constraints. ACM KDD conference, pp 138–147

Gouda K, Zaki M (2001) Efficiently mining maximal frequent itemsets. ICDM conference, pp 163–170

Han J (1996a) Background for association rules and cost estimate of selected mining algorithms. ACM CIKM, pp 73–80

Han J (1996b) Pushing constraints in templates for mining association rules. Florida AI research symposium, pp 375–379

Han J, Kamber M (2001) Data mining: Concepts and techniques, 1st edn. Morgan Kaufmann, San Francisco

Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. ACM SIGMOD Conference, pp 1–12

Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning, 1st edn. Springer, New York

Klemettinen M, Mannila H, Ronkainen P, Toivonen H, Verkamo A (1994) Finding interesting rules from large sets of discovered association rules. ACM CIKM, pp 401–407

Kryszkiewicz M (2001) Concise representation of frequent patterns based on disjunction-free generators. IEEE ICDM conference, pp 305–312

Kryszkiewicz M (2004) Reducing borders of k-disjunction free representations of frequent patterns. ACM SAC conference, pp 559–563

Lakshmanan LV, Ng R, Han J, Pang A (1999) Optimization of constrained frequent set queries with 2-variable constraints. ACM SIGMOD conference, pp 157–168

Lent B, Swami A, Widom J (1997) Clustering association rules. IEEE ICDE conference, pp 220–231

Lin D, Kedem Z (1998) Pincer-search: a new algorithm for discovering the maximum frequent itemset. EDBT conference, pp 105–119

Long W (1989) Medical reasoning using a probabilistic network. Appl Artif Intell 3:367–383

Long W, Fraser H, Naimi S (1997) Reasoning requirements for diagnosis of heart disease. Artif Intell Med 10(1):5–24

Ng R, Lakshmanan L, Han J (1998) Exploratory mining and pruning optimizations of constrained association rules. ACM SIGMOD conference, pp 13–24

Ordonez C, Omiecinski E, de Braal L, Santana C, Ezquerra N (2001) Mining constrained association rules to predict heart disease. IEEE ICDM conference, pp 433–440

Ordonez C, Santana C, Braal L (2000) Discovering interesting association rules in medical data. ACM DMKD workshop, pp 78–85

Oyama T, Kitano K, Satou T, Ito T (2002) Extraction of knowledge on protein–protein interaction by association rule discovery. Bioinformatics 18(5):705–714

Pasquier N, Bastide Y, Taouil RG, Lakhal L (1999) Discovering frequent closed itemsets for association rules. ICDT conference, pp 398–416

Pei J, Han J (2002) Constraints in data mining: constrained frequent pattern mining: a pattern-growth view. SIGKDD Explor 4(1):31–39

Pei J, Han J, Mao R (2000) CLOSET: an efficient algorithm for mining frequent closed itemsets. ACM DMKD workshop, pp 21–30

Phan-Luong V (2001) The representative basis for association rules. IEEE ICDM, pp 639–640

Pudi V, Haritsa J (2003) Reducing rule covers with deterministic error bounds. PAKDD conference, pp 313–324

Rastogi R, Shim K (1998) Mining optimized association rules with categorical and numeric attributes. IEEE ICDE conference, pp 503–512

Roddick J, Fule P, Graco W (2003) Exploratory medical knowledge discovery: experiences and issues. SIGKDD Explor 5(1):94–99

Srikant R, Agrawal R (1995) Mining generalized association rules. VLDB conference, pp 407–419

Srikant R, Agrawal R (1996) Mining quantitative association rules in large relational tables. ACM SIGMOD conference, pp 1–12

Srikant R, Vu Q, Agrawal R (1997) Mining association rules with item constraints. ACM KDD conference, pp 67–73

Taouil R, Pasquier N, Bastide Y, Lakhal L (2000) Mining bases for association rules using closed sets. IEEE ICDE conference, p 307

Wang K, He Y, Han J (2003) Pushing support constraints into association rules mining. IEEE TKDE 15(3):642–658