Inverted matrix: efficient discovery of frequent items in large datasets in the context of interactive mining

ABSTRACT

Existing association rule mining algorithms suffer from many problems when mining massive transactional datasets. One major problem is the high memory dependency: either the gigantic data structure built is assumed to fit in main memory, or the recursive mining process is too voracious in memory resources. Another major impediment is the repetitive and interactive nature of any knowledge discovery process. To tune parameters, many runs of the same algorithms are necessary leading to the building of these huge data structures time and again. This paper proposes a new disk-based association rule mining algorithm called Inverted Matrix, which achieves its efficiency by applying three new ideas. First, transactional data is converted into a new database layout called Inverted Matrix that prevents multiple scanning of the database during the mining phase, in which finding frequent patterns could be achieved in less than a full scan with random access. Second, for each frequent item, a relatively small independent tree is built summarizing co-occurrences. Finally, a simple and non-recursive mining process reduces the memory requirements as minimum candidacy generation and counting is needed. Experimental studies reveal that our Inverted Matrix approach outperform FP-Tree especially in mining very large transactional databases with a very large number of unique items. Our random access disk-based approach is particularly advantageous in a repetitive and interactive setting.

REFERENCES

• 1: Rakesh Agrawal , Tomasz Imieliński , Arun Swami, Mining association rules between sets of items in large databases, Proceedings of the 1993 ACM SIGMOD international conference on Management of data, p.207-216, May 25-28, 1993, Washington, D.C., United States
• 2: Rakesh Agrawal , Ramakrishnan Srikant, Fast Algorithms for Mining Association Rules in Large Databases, Proceedings of the 20th International Conference on Very Large Data Bases, p.487-499, September 12-15, 1994
• 3: IBM. Almaden. Quest synthetic data generation code. http://www.almaden.ibm.com/cs/quest/syndata.html.
• 4: Maria-Luiza Antonie , Osmar R. Zaïane, Text Document Categorization by Term Association, Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM'02), p.19, December 09-12, 2002
• 5: C. Borgelt. Apriori implementation. http://fuzzy.cs.uni- magdeburg.de/~borgelt/apriori/apriori.html.
• 6: Sergey Brin , Rajeev Motwani , Jeffrey D. Ullman , Shalom Tsur, Dynamic itemset counting and implication rules for market basket data, Proceedings of the 1997 ACM SIGMOD international conference on Management of data, p.255-264, May 11-15, 1997, Tucson, Arizona, United States
• 7: Eui-Hong (Sam) Han , George Karypis , Vipin Kumar, Scalable Parallel Data Mining for Association Rules, IEEE Transactions on Knowledge and Data Engineering, v.12 n.3, p.337-352, May 2000 [doi>10.1109/69.846289 ]
• 8: Jiawei Han , Micheline Kamber, Data mining: concepts and techniques, Morgan Kaufmann Publishers Inc., San Francisco, CA, 2000
• 9: Jiawei Han , Jian Pei , Yiwen Yin, Mining frequent patterns without candidate generation, Proceedings of the 2000 ACM SIGMOD international conference on Management of data, p.1-12, May 15-18, 2000, Dallas, Texas, United States
• 10: Jochen Hipp , Ulrich Güntzer , Gholamreza Nakhaeizadeh, Algorithms for association rule mining — a general survey and comparison, ACM SIGKDD Explorations Newsletter, v.2 n.1, p.58-64, June, 2000 [doi>10.1145/360402.360421]
• 11: Hao Huang , Xindong Wu , Richard Relue, Association Analysis with One Scan of Databases, Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM'02), p.629, December 09-12, 2002
• 12: Junqiang Liu , Yunhe Pan , Ke Wang , Jiawei Han, Mining frequent item sets by opportunistic projection, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada [doi>10.1145/775047.775081]
• 13: Jong Soo Park , Ming-Syan Chen , Philip S. Yu, An effective hash-based algorithm for mining association rules, Proceedings of the 1995 ACM SIGMOD international conference on Management of data, p.175-186, May 22-25, 1995, San Jose, California, United States
• 14: Mining Recurrent Items in Multimedia with Progressive Resolution Refinement, Proceedings of the 16th International Conference on Data Engineering, p.461, February 28-March 03, 2000
• 15: M. Zaki, S. Parthasarathy, M. Ogihara, and W. Li. New algorithms for fast discovery of association rules. In 3rd Intl. Conf. on Knowledge Discovery and Data Mining, 1997.
• 16: Mohammed J. Zaki, Parallel and Distributed Association Mining: A Survey, IEEE Concurrency, v.7 n.4, p.14-25, October 1999 [doi>10.1109/4434.806975 ]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications

Keywords:
COFI-tree, association rules, frequent patterns mining, inverted matrix