T. Fukuda, Y. Morimoto, S. Morishita, and T. Tokuyama. Mining Optimized Asso- ciation Rules for Numeric Attributes. In Proc. of the 15th Symp. on Principles of Database Systems, pages 182 191, Montreal, Canada, June 1996.
12541 D. Gunopulos, R. Khardon, H. Mannila, and H. Toivonen. Data Mining, Hypergraph TYansversals, and Machine Learning. In Proc. of the 16th Sgmp. on Princi,ples of Database Sgstems, pages 209-216, T\rcson, AZ,May 1997.
[255] E.-H. Han, G. Karypis, and V. Kumar. Min-Apriori: An Algorithm for Finding As- sociation Rules in Data with Continuous Attributes. http://www.cs.umn.edu/-han, L997.
[256] E.-H. Han, G. Karypis, and V. Kumar. Scalable Parallel Data Mining for Association Rules. In Proc. of 1997 ACM-SIGMOD IntI. Conf. on Management of Data, pages
277-288. T\rcson. AZ.Mav 1997.
Bibliography 399
12571 E.-IJ. Han, G. Karypis, V. Kumar, and B. Mobasher. Clustering Based on Association Rule Hypergraphs. In Proc. of the 1997 ACM SIGMOD Workshop on Research Issues in Data Mi,ning and, Knowledge D’iscoaery, Tucson, AZ, 1997.
[258] J. Han, Y. Fu, K. Koperski, W. Wang, and O. R. Zaiane. DMQL: A data mining query language for relational databases. In Proc. of the 1996 ACM SIGMOD Workshop on Research Issues in Data Mi,ni,ng and Knowledge Discouery, Montreal, Canada, June 1996.
[259] J. Han, J. Pei, and Y. Yin. Mining Frequent Patterns without Candidate Generation. In Proc. ACM-SIGMOD Int. Conf. on Management of Data (SIGMOD’00), pages 1-12, Dallas, TX, May 2000.
[260] C. Hidber. Online Association Rule Mining. ln Proc. of 1999 ACM-SIGMOD IntL Conf. on Managernent of Data, pages 145-156, Philadelphia, PA, 1999.
[261] R. J. Hilderman and H. J. Hamilton. Knouledge Discouery and Measures of Interest. Kluwer Academic Publishers, 2001
12621 J. Hipp, U. Guntzer, and G. Nakhaeizadeh. Algorithms for Association Rule Mining- A General Survey. Si,gKDD Erplorations,2(1):58-64, June 2000.
[263] H. Hofmann, A. P. J. M. Siebes, and A. F. X. Wilhelm. Visualizing Association Rules with Interactive Mosaic Plots. In Proc. of the 6th IntI. Conf. on Knowledge Discouerg
– and, Data Mining, pages 227-235, Boston, MA, August 2000.
12641 J. D. Holt and S. M. Chung. Efficient Mining of Association Rules in Text Databases. In Proc. of the 8th IntI. Conf. on Inforrnation and, Knowledge Management, pages 234-242, Kansas City Missouri, 1999.
[265] M. Houtsma and A. Swami. Set-oriented Mining for Association Rules in Relational Databases. In Proc. of the 11th IntI. Conf. on Data Eng,ineering, pages 25 33, Taipei, Taiwan, 1995.
[266] Y. Huang, S. Shekhar, and H. Xiong. Discovering Co-location Patterns from Spatial Datasets: A General Approach. IEEE Tfans. on Knowledge and, Data Engineering, L6 (12) :1472-1485, December 2004.
12671 T.Imielinski, A. Virmani, and A. Abdulghani. DataMine: Application Programming Interface and Query Language for Database Mining. In Proc. of the 2nd Intl. Conf. on Knowledge D’iscouerg and Data Mi,n’ing, pages 256-262, Portland, Oregon, 1996.
[268] A. Inokuchi, T. Washio, and H. Motoda. An Apriori-based Algorithm for Mining Frequent Substructures from Graph Data. In Proc. of the lth European Conf. of Prin- ci,ples and Practice of Knowledge Discouery i,n Databases, pages 13 23, Lyon, Fbance, 2000.
f269] S. Jaroszewicz and D. Simovici. Interestingness of Flequent Itemsets Using Bayesian Networks as Background Knowledge. In Proc. of the 10th Intl. Conf. on Knowled”ge Discouerg and Data Min’ing, pages 178-186, Seattle, WA, August 2004.
1270] M. Kamber and R. Shinghal. Evaluating the Interestingness of Characteristic Rules. In Proc. of the Znd Intl. Conf. on Knowledge Di,scouerE and Data Min’ing, pages 263-266, Portland, Oregon, 1996.
l27ll M. Klemettinen. A Knowleilge Di,scoaerg Methodologg for Telecornrnunicat’ion Network Alarm Databases. PhD thesis, University of Helsinki, 1999.
1272) W. A. Kosters, E. Marchiori, and A. Oerlemans. Mining Clusters with Association Rules. In The 9rd, SEmp. on Intelligent Data AnalEsis (IDA99), pages 39-50, Amster- dam, August 1999.
12731 C. M. Kuok, A. Fu, and M. H. Wong. Mining Fuzzy Association Rules in Databases. ACM SIGMOD Record,27(l):47-46, March 1998.
4OO Chapter 6 Association Analysis
1274] M. Kuramochi and G. Karypis. Frequent Subgraph Discovery. In Proc. of the 2001 IEEE Intl. Conf. on Data Mi,ning, pages 313-320, San Jose, CA, November 2001.
1275] W. Lee, S. J. Stolfo, and K. W. Mok. Adaptive Intrusion Detection: A Data Mining Approach. Artificial Intelligence Reu’iew, 14(6) :533-567, 2000.
1276] W. Li, J. Han, and J. Pei. CMAR: Accurate and Efficient Classification Based on Multiple Class-association Rules. In Proc. of the 2001 IEEE IntI. Conf. on Data M’ining, pages 369 376, San Jose, CA, 2001.
12771 B. Liu, W. Hsu, and S. Chen. Using General Impressions to Analyze Discovered Classification Rules. In Proc. of the Srd Intl. Conf. on Knowledge Discouery and Data Mining, pages 31-36, Newport Beach, CA, August 1997.
12781 B. Liu, W. Hsu, and Y. Ma. Integrating Classification and Association Rule Mining. In Proc. of the lth IntI. Conf. on Knowledge D’iscouery and, Data M’ini,ng, pages 80-86, New York, NY, August 1998.
1279] B. Liu, W. Hsu, and Y. Ma. Mining association rules with multiple minimum supports. In Proc. of the Sth Intl. Conf. on Knowledge Discouerg and Data Mining, pages 125 134, San Diego, CA, August 1999.
1280] B. Liu, W. Hsu, and Y. Ma. Pruning and Summarizing the Discovered Associations. In Proc. of theSthIntI. Conf. onKnowledgeDiscoueryandDataMining, pages125 134, San Diego, CA, August 1999.
1281] A. Marcus, J. L Maletic, and K.-I. Lin. Ordinal association rules for error identifi- cation in data sets. In Proc. of the 10th Intl. Conf. on Inforrnation and, Knowledge Management, pages 589-591, Atlanta, GA, October 2001.
[282] N. Megiddo and R. Srikant. Discovering Predictive Association Rules. In Proc. of the
Ith Intl. Conf. on Knowled,ge Discouery and Data Min’ing, pages 274-278, New York, August 1998.
[283] R. Meo, G. Psaila, and S. Ceri. A New SQL-like Operator for Mining Association Rules. In Proc. of the 22nd VLDB Conf., pages I22 133, Bombay, India, 1-996.
[284j R. J. Miller and Y. Yang. Association Rules over Interval Data. In Proc. of 1997 ACM-SIGMOD Intl. Conf. on Management of Data, pages 452-461, T\rcson, LZ,May 1997.
[285] Y. Morimoto, T. Fukuda, H. Matsuzawa, T. Tokuyama, and K. Yoda. Algorithms for mining association rules for binary segmentations of huge categorical databases. In Proc. of the 2lth VLDB Conf., pages 380-391, New York, August 1998.
[286] F. Mosteller. Association and Estimation in Contingency Tables. Journal of the Amer- ican Statistical Association 63:1-28. 1968,
12871 A. Mueller. Fast sequential and parallel algorithms for association rule mining: A comparison. Technical Report CS-TR-3515, University of Maryland, August 1995.
[288] R. T. Ng, L. V. S. Lakshmanan, J. Han, and A. Pang. Exploratory Mining and Pruning Optimizations of Constrained Association Rules. In Proc. of 1998 ACM-SIGMOD IntI. Conf. on Management of Data, pages 13-24, Seattle, WA, June 1998.
1289] E. Omiecinski. Alternative Interest Measures for Mining Associations in Databases. IEEE Ttans. on Knowledge and” Data Engineering, 15(1):57-69, January/February 2003.
[290] B. Ozden, S. Ramaswamy, and A. Silberschatz. Cyclic Association Rules. In Proc. of the llth IntI. Conf. on Data Eng., pages 412 42I, Orlando, FL, February 1998.
[291] A. Ozgur, P. N. Tan, and V. Kumar. RBA: An Integrated Framework for Regression based on Association Rules. In Proc. of the SIAM IntI. Conf. on Data M’ining, pages 2 M 2 7 , O r l a n d o , F L , A p r i l 2 0 0 4 .
Bibliography 4OL
1292] J. S. Park, M.-S. Chen, and P. S. Yu. An efiective hash-based algorithm for mining association rrles. SIGMOD Record,25(2):175 186, 1995.
[293] S. Parthasarathy and M. Coatney. Efficient Discovery of Common Substructures in Macromolecules. In Proc. of the 2002 IEEE IntI. Conf. on Data M’ining, pages 362-369, Maebashi City, Japan, December 2002.
[294] N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Discovering frequent closed itemsets for association rules. In Proc. of the 7th Intl. Conf. on Database Theory 0CDT’99), pages 398 416, Jerusalem, Israel, January 1999.
[295] J. Pei, J. Han, H. J. Lu, S. Nishio, and S. Tang. H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases. In Proc. of the 2001 IEEE Intl. Conf. on Data M’ining, pages 441-448, San Jose, CA, November 2001.
[296] J. Pei, J. Han, B. Mortazavi-Asl, and H. Zhtt. Mining Access Patterns Efficiently from Web Logs. In Proc. of the lth Pacific-Asia Conf. on Knowledge Discouery and” Data Mining, pages 396-407, Kyoto, Japan, April 2000.
1297] G. Piatetsky-Shapiro. Discovery, Analysis and Presentation of Strong Rules. In G. Piatetsky-Shapiro and W. Frawley, editors, Knowledge Discouery in Databases, pages 229-248. MIT Press, Cambridge, MA, 1991.
[298] C. Potter, S. Klooster, M. Steinbach, P. N. Tan, V. Kumar, S. Shekhar, and C. Car- valho. Understanding Global Teleconnections of Climate to Regional Model Estimates of Amazon Ecosystem Carbon Fluxes. Global Change Biology, 70(5):693-703, 20A4.
[299] C. Potter, S. Klooster, M. Steinbach, P. N. Tan, V. Kumar, S. Shekhar, R. Myneni, and R. Nemani. Global Teleconnections of Ocean Climate to Terrestrial Carbon Flux. J. Geophysical Research, 108(D17), 2003.
1300] G. D. Ramkumar, S. Ranka, and S. Tsur. Weighted Association Rules: Model and Algorithm. http: //www.cs.ucla.edu/
“c zdemo f tsw f , 1997 .
13o1lS. Sarawagi, S. Thomas, and R. Agrawal. Integrating Mining with Relational Database Systems: Alternatives and Implications. In Proc. of 1998 ACM-SIGMOD IntI. Conf. on Management of Data, pages 343-354, Seattle, WA, 1998. K. Satou, G. Shibayama, T, Ono, Y. Yamamura, E. Furuichi, S. Kuhara, and T. Takagi. Finding Association Rules on Heterogeneous Genome Data. In Proc. of the Pacific Symp. on Biocomputing, pages 397-408, Hawaii, January 1997.
[303] A. Savasere, E. Omiecinski, and S. Navathe. An efficient algorithm for mining associ- ation rules in large databases. In Proc. of the 21st Int. Conf. on Very Large Databases (VLDB’gs), pages 432-444, Z:uicln, Switzerland, September 1995.
1304] A. Savasere, E. Omiecinski, and S. Navathe. Mining for Strong Negative Associations in a Large Database of Customer Tbansactions. In Proc. of the llth Intl. Conf. on Data Engineering, pages 494 502, Orlando, Florida, February 1998.
[305] M. Seno and G. Karypis. LPMiner: An Algorithm for Finding FYequent Itemsets Using Length-Decreasing Support Constraint. In Proc. of the 2001 IEEE Intl. Conf. on Data Min’ing, pages 505-512, San Jose, CA, November 2001.
[306] T. Shintani and M. Kitsuregawa. Hash based parallel algorithms for mining association rules. In Proc of the lth IntI. Conf . on Parallel and, Di;stributed, Info. Sgstems, pages 19-30, Miami Beach, FL, December 1996.
[307] A. Silberschatz and A. Tirzhilin. What makes patterns interesting in knowledge discov- ery systems. IEEE Trans. on Knowledge and Data Engineering,8(6):970-974, 1996.
[308] C. Silverstein, S. Brin, and R. Motwani. Beyond market baskets: Generalizing associ- ation rules to dependence rules. Data Mining and Knowledge D’iscouery, 2(1):39-68, 1998.
[302]
4O2 Chapter 6 Association Analysis
f309] E.-H. Simpson. The Interpretation of Interaction in Contingency Tables. Journal of the Rogal Stati,stical Societg, B(13):238-241, 1951.
1310] L. Singh, B. Chen, R. Haight, and P. Scheuermann. An Algorithm for Constrained Association Rule Mining in Semi-structured Data. In Proc. of the ?rd Pacific-Asi,a Conf. on Knouled.ge Di,scouery and Data M’ining, pages 148-158, Beijing, China, April 1999.
1311] R. Srikant and R. Agrawal. Mining Quantitative Association Rules in Large Relational Tables. In Proc. of 1996 ACM-SIGMOD IntI. Conf. on Management of Data, pages
1-12, Montreal, Canada, 1996.
[312] R. Srikant and R. Agrawal. Mining Sequential Patterns: Generalizations and Perfor- mance Improvements. In Proc. of the 5th IntI Conf. on Ertend’ing Database Technologg (EDBT’96), pages 18 32, Avignon, France, 1996.
[313] R. Srikant, Q. Vu, and R. Agrawal. Mining Association Rules with Item Constraints. In Proc. of the 9rd IntI. Conf. on Knowledge D’iscouery and Data Mining, pages 67-73, Newport Beach, CA, August 1997.
1314] M. Steinbach, P. N. Tan, and V. Kumar. Support Envelopes: A Technique for Ex- ploring the Structure of Association Patterns. In Proc. of the 10th Intl. Conf. on Knowled,ge D’iscouery and, Data Min’ing, pages 296 305, Seattle, WA, August 2004.
1315] M. Steinbach, P. N. Tan, H. Xiong, and V. Kumar. Extending the Notion of Support. In Proc. of the 10th IntI. Conf. on Knowledge Discoaerg and Data Mining, pages 689- 694, Seattle, WA, August 2004.
[316] E. Suzuki. Autonomous Discovery of Reliable Exception Rules. In Proc. of the ?rd Intl. Conf. on Knowled,ge Discouery and Data Mi,ning, pages 259-262, Newport Beach, CA, August 1997.
[317] P. N. Tan and V. Kumar. Mining Association Patterns in Web Usage Data. In Proc. of the IntI. Conf. on Ad”uances ‘in Infrastructure for e-Business, e-Ed”ucation, e-Science and e-Medi,ci,ne on the Internet, L’Aquila, Italy, January 2002.
1318] P. N. Tan, V. Kumar, and J. Srivastava. Selecting the Right Interestingness Measure for Association Patterns. In Proc. of the Sth Intl. Conf. on Knowledge D’iscouery and, Data Mining, pages 32-41, Edmonton, Canada, JuJy 2002.
[319] P. N. Tan, M. Steinbach, V. Kumar, S. Klooster, C. Potter, and A. Torregrosa. Finding Spatio.Temporal Patterns in Earth Science Data. In KDD 2001 Workshop on Temporal Data Mi,ni,ng, San Francisco, CA, 2001.
[320] H. Toivonen. Sampling Large Databases for Association Rules. In Proc. of the 22nd VLDB Conf., pages 134-145, Bombay, India, 1996.
1321] H. Toivonen, M. Klemettinen, P. Ronkainen, K. Hatonen, and H. Mannila. Pruning and Grouping Discovered Association Rules. In ECML-95 Workshop on Statist’ics, Machine Learning and, Knowledge D’iscouery ‘in Databases, pages 47 – 52, Heraklion, Greece, April 1995.
[322] S. Tsur, J. Ullman, S. Abiteboul, C. Clifton, R. Motwani, S. Nestorov, and A. Rosen- thal. Query Flocks: A Generalization of Association Rule Mining. In Proc. of 1998 ACM-SIGMOD Intl. Conf. on Management of Data, pages 1-12, Seattle, WA, June 1998.
[323] A. Tung, H. J. Lu, J. Han, and L. Feng. Breaking the Barrier of TYansactions: Mining Inter-TYansaction Association Rules. In Proc. of the Sth Intl. Conf. on Knowledge Discouery and, Data Mining, pages 297-301, San Diego, CA, August 1999.
[324] K. Wang, Y. He, and J. Han. Mining Frequent Itemsets Using Support Constraints. In Proc. of the 26th VLDB Conf., pages 43 52, Cairo, Egypt, September 2000.
BIBLIOGRAPHY 4O3
[325] K. Wang, S. H. Tay, and B. Liu. Interestingness-Based Interval Merger for Numeric Association Rules. In Proc. of the lth IntL Conf. on Knowledge Discouerg and Data Min’ing, pages 121-128, New York, NY, August 1998.
[326] G. I. Webb. Preliminary investigations into statistically valid exploratory rule dis- covery. In Proc. of the Australasian Data Mi,ni,ng Workshop (AusDM1?), Canberra, Australia, December 2003.
