Abstract
Closed itemset mining is a difficult problem especially when we consider the task in the context of a data stream. Compared to mining from a static transaction data set, the streaming case has far more information to track and far greater complexity to manage. In this paper, we propose a complete solution based on CLOSET+ algorithm to closed itemset mining in data streams. In data streams, bounded memory and one-pass constraint are expected. In our solution, these constraints are both taken into account.
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Giannella, C., Han, J., Pei, J., Yan, X., Yu, P.S.: Mining Frequent Patterns in Data Streams at Multiple Time Granularities. In: Kargupta, H., Joshi, A., Sivakumar, K., Yesha, Y. (eds.) Next Generation Data Mining, AAAI/MIT (2003)
Pei, J., Han, J., Wang, J.: Closet+: Searching for the best strategies for mining frequent closed itemsets. In: SIGKDD 2003 (August 2003)
Liu, J., Pan, Y., Wang, K., Han, J.: Mining frequent item sets by opportunistic projection. In: SIGKDD 2002 (July 2002)
Zaki, M., Hsiao, C.: CHARM: An efficient algorithm for closed itemset mining. In: SDM 2002 (April 2002)
Burdick, D., Calimlim, M., Gehrke, J.: MAFIA: A maximal frequent itemset algorithm for transactional databases. In: ICDE 2001 (April 2001)
Han, E., Karypis, G., Kumar, V.: Scalable Parallel Data Mining for Association Rules. In: TKDE, vol. 12(2) (2000)
Pei, J., Han, J., Mao, R.: CLOSET: An efficient algorithm for mining frequent closed itemsets. In: DMKD 2000 (May 2000)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: SIGMOD 2000 (May 2000)
Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering frequent closed itemsets for association rules. In: ICDT 1999 (January 1999)
Bayardo, R.J.: Efficiently Mining long patterns from databases. In: SIGMOD 1998 (June 1998)
Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic Itemset Counting and Implication Rules for Market Basket Data. In: SIGMOD 1997 (May 1997)
Gunopulos, D., Mannila, H., Saluja, S.: Discovering All Most Specific Sentences by Randomized Algorithms. In: ICDT 1997 (January 1997)
Toivonen, H.: Sampling Large Databases for Association Rules. In: VLDB 1996 (September 1996)
Park, J., Chen, M., Yu, P.S.: An Effective Hash Based Algorithm for Mining Association Rules. In: SIGMOD 1995 (May 1995)
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: SIGMOD 1993 (May 1993)
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© 2005 Springer-Verlag Berlin Heidelberg
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Wang, H., Li, W., Li, Z., Fan, L. (2005). Finding Closed Itemsets in Data Streams. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552451_133
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DOI: https://doi.org/10.1007/11552451_133
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28895-4
Online ISBN: 978-3-540-31986-3
eBook Packages: Computer ScienceComputer Science (R0)