Abstract
The paper proposed a fast distributed mining algorithm of maximum frequent itemsets based on cloud computing, namely, FDMMFI algorithm. FDMMFI algorithm made nodes compute local maximum frequent itemsets by cloud computing, then the center node exchanged data with other nodes and combined, finally, global maximum frequent itemsets were gained by cloud computing. Theoretical analysis and experimental results suggest that under the same minimum support threshold, communication traffic and runtime of FDMMFI decreases while comparing with CD and FDM. The less the minimum support threshold, the better the three performance parameters of FDMMFI.FDMMFI algorithm is fast and effective.
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References
Mao, Y.X., Le Shi, B.: AFOPT-tax: An efficient method for mining generalized frequent itemsets. In: Nguyen, N.T., Le, M.T., Świątek, J. (eds.) ACIIDS 2010. LNCS, vol. 5990, pp. 82–92. Springer, Heidelberg (2010)
Bayardo, R.J.: Efficiently mining long patterns form databases. In: Haas, L.M., Tiwary, A. (eds.) Proc. of the ACM SIGMOD International Conference on Management of Data, pp. 1–12. ACM Press, Dallas (2000)
Song, Y.Q., Zhu, Z.H., Chen, G.: An algorithm and its updating algorithm based on FP-tree for mining maximum frequent itemsets. Journal of Software 14(9), 1586–1592 (2003) (in Chinese with English abstract)
Agrawal, R., Shafer, J.C.: Parallel mining of association rules. IEEE Transaction on Knowledge and Data Engineering 8(6), 962–969 (1996)
Cheung, D.W., Han, J.W., Ng, W.T., Tu, Y.J.: A fast distributed algorithm for mining association rules. In: Proceedings of IEEE 4th International Conference on Management of Data, Miami Beach, Florida, pp. 31–34 (1996)
He, B.: Fast Mining of Global Maximum Frequent Itemsets in Distributed Database. Control and Decision 26(8), 1214–1218 (2011) (in Chinese with English abstract)
Li, J., Khuller, A.D.S: On computing compression trees for data collection in wireless sensor networks. In: Proc. of the IEEE INFOCOM 2010, pp. 2115–2123. IEEE Press, Washington (2010)
He, B., Yan, H.: Incremental Updating Algorithm of Global Maximum Frequent Itemsets in Distributed Database. Journal of Sichuan University (Engineering Science Edition) 44(3), 112–117 (2012) (in Chinese with English abstract)
Park, J.S., Chen, M.S., Yu, P.S.: Efficient parallel data mining for association rules. In: Proceedings of the 4th International Conference on Information and Knowledge Management, Baltimore, Maryland, pp. 31–36 (1995)
He, B.: Distributed Algorithm for Mining Association Rules Based on FP-tree. Control and Decision 27(4), 618–622 (2012) (in Chinese)
Tao, L.M., Huang, L.P.: Cherry: An Algorithm for Mining Frequent Closed Itemsets without Subset Checking. Journal of Software 19(2), 379–388 (2008) (in Chinese with English abstract)
Wang, L.H., Zhao, H.: Algorithms of Mining Global Maximum Frequent Itemsets Based on FP-Tree. Journal of Computer Research and Develpment 44(3), 445–451 (2007) (in Chinese with English abstract)
Aggarwal, C., Yu, P.: Outlier detection for high dimensional data. In: Proc. of SIGMOD 2001, pp. 37–47 (2001)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proc. of VLDB 1994, pp. 487–499 (1994)
Barnett, V., Lewis, T.: Outliers In Statistical Data. John Wiley, Reading (1994)
Breunig, M.M., Kriegel, H.-P., Ng, R.T., Sander, J.: OPTICS-OF: Identifying local outliers. In: Żytkow, J.M., Rauch, J. (eds.) PKDD 1999. LNCS (LNAI), vol. 1704, pp. 262–270. Springer, Heidelberg (1999)
Breunig, M., Kriegel, H.-P., Ng, R., Sander, J.: Lof: Identifying density-based local outliers. In: Proc. of SIGMOD 2000, pp. 93–104 (2000)
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc. of KDD 1996, pp. 226–231 (1996)
Guha, S., Rastogi, R., Shim, K.: Cure: An efficient clustering algorithm for large databases. In: Proc. of SIGMOD 1998, pp. 73–84 (1998)
Hawkins, D.: Identification of Outliers. Chapman and Hall, Reading (1980)
Hussain, F., Liu, H., Tan, C.L., Dash, M.: Discretization: An enabling technique. Technical Report TRC6/99, National University of Singapore, School of Computing (1999)
Jin, W., Tung, A.K., Han, J.: Mining top-n local outliers in large databases. In: Proc. of KDD 2001, pp. 293–298 (2001)
Knorr, E., Ng, R.: Algorithms for mining distance-based outliers in large datasets. In: Proc. of VLDB 1998, pp. 392–403 (1998)
Knorr, E., Ng, R.: Finding intensional knowledge of distance-based outliers. In: Proc. of VLDB 1999, pp. 211–222 (1999)
Merz, G., Murphy, P.: Uci repository of machine learning databases. Technical Report, University of California, Department of Information and Computer Science (1996), http://www.ics.uci.edu/mlearn/MLRepository.html
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He, B. (2013). Fast Distributed Mining Algorithm of Maximum Frequent Itemsets Based on Cloud Computing. In: Yang, Y., Ma, M., Liu, B. (eds) Information Computing and Applications. ICICA 2013. Communications in Computer and Information Science, vol 391. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53932-9_40
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DOI: https://doi.org/10.1007/978-3-642-53932-9_40
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