Abstract
During the past two decades, frequent pattern mining (FPM) has acquired the interests of many researchers: which involves extracting the itemsets from transactions, sequences from big dataset, which occurs frequently and to recognize from the molecular structures, the common sub graph. In this big data era, the unpredictable flow and huge quantity of data brings new challenges in FPM such as space and time complexity. In general, most of the research work focus on recognizing the patterns that occurs frequently, from the set of specific data, where the patterns within every transaction were definitely known a priori. Among these, the users focus only on the small part of this FP. In order to tackle such problems in the current scenario, it is necessary sometimes to select the important features alone, using appropriate FPM algorithms, in order to reduce the complexity level. The major objective of this work is to improve FPM mining results and improve classification accuracy of big dataset samples. To tackle the first challenge, the levy flight bat algorithm (LFBA) along with online feature selection (OFS) approach is proposed, which is used to filter the low quality features from the big data in an online manner. Subsequently to address the second challenge, a weighted entropy frequent pattern mining (WEFPM) is enforced for FPM, to accomplish better computation time when compared with other methods such as direct discriminative pattern mining (DDPMine) and iterative sampling based frequent itemset mining (ISbFIM), where enumeration of entire feature combinations were completed. So the WEFPM algorithm employed in this paper, targets to identify only the specific frequent patterns which are required by the user. By iterating this procedure, it assures that the acquired frequent patterns can be enumerated by using both the theoretical and empirical research, so that enumeration doesn’t proceed into a combinatorial explosion. And also, using the above said LFBA–OFS approach and WEFPM algorithm, frequent patterns that are different in nature, are generated for building high quality learning model. For finding the frequent patterns, here the minimum support threshold is matched with entropy. As a final step, multiple Kernel learning support vector machine is employed as a classifier, to evaluate the performance of the big data samples for efficiency and accuracy. Empirical study reveal that considerable progress is obtained in terms of accuracy and computation time when applied to UCI benchmark big datasets, using the proposed approach for efficient and effective FPM of the online features. It is clear that WEFPM is the most efficient method, because it produces higher average accuracy results of 92.34, 93.218, 91.374 and 87.87% values for adult, chess, hybo and sick dataset respectively. It outperforms when compared to other methods such as DDPMine and ISbFIM using an LIBSVM classifier.
Similar content being viewed by others
References
Cai, C.H., Fu, A.W.C., Cheng, C.H., Kwong, W.W.: Mining association rules with weighted items. In: International Database Engineering and Applications Symposium, 1998 (IDEAS’98), pp. 68–77 (1998)
Zaki, M.J., Hsiao, C.: CHARM: an efficient algorithm for closed itemset mining. In: Proc. of SDM, pp. 457–473 (2002)
Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.-C.: PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. In: Proc. of ICDE, pp. 215–226 (2001)
Washio, T., Motoda, H.: State of the art of graph-based data mining. ACM SIGKDD Explor. Newsl. 5(1), 59–68 (2003)
Yan, X., Yu, P.S., Han, J.: Graph indexing: a frequent structure-based approach. In: Proc. of SIGMOD, pp. 335–346 (2004)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2012)
Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Commun. ACM 51(107–113), 12 (2008)
Fan, W., Zhang, K., Cheng, H., Gao, J., Yan, X., Han, J., Yu, P., Verscheure, O.: Direct mining of discriminative and essential frequent patterns via model-based search tree. In: Proceeding of KDD ’08, pp. 230–238. ACM, New York (2008)
Shintani, T., Kitsuregawa, M.: Parallel mining algorithms for generalized association rules with classification hierarchy. ACM SIGMOD Record 27(2), 25–36 (1998)
Borgelt, C., Kruse, R.: Induction of association rules: a priori implementation. In: Compstat, pp. 395–400 (2002)
Pan F., Cong, G., Tung, A.K.H., Yang, J., Zaki, M.J.: CARPENTER: finding closed patterns in long biological datasets. In: Proc. 2003 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (2003)
Pan, F., Tung, A.K.H., Cong, G., Xu, X.: COBBLER: combining column and row enumeration for closed pattern discovery. In: Proc. 2004 Int. Conf. on Scientific and Statistical Database Management (SSDBM’04), Santorini Island, Greece, pp. 21–30 (2004)
Cong, G., Tan, K.-L., Tung, A.K.H., Xu, X.: Mining top-k covering rule groups for gene expression data. In: 24th ACM International Conference on Management of Data (2005)
Lin, M.Y., Lee, P.Y., Hsueh, S.C.: Apriori-based frequent item set mining algorithms on mapreduce. In: Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication, ICUIMC’12, pp 76:1–76:8. ACM, New York (2012)
Zaki, M., Parthasarathy, S., Ogihara, M., Li, W.: Parallel algorithms for discovery of association rules. Data Min. Knowl. Discov. 1, 343–373 (1997)
Li, H., Wang, Y., Zhang, D., Zhang, M., Chang, E.Y.: PFP: parallel FP-growth for query recommendation. In: Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys’08, pp. 107–114. ACM, New York (2008)
Yang, G.: Computational aspects of mining maximal frequent patterns. Theor. Comput. Sci. 362(1–3), 63–85 (2006)
Wang, J., Zhao, P., Hoi, S.C., Jin, R.: Online feature selection and its applications. IEEE Trans. Knowl. Data Eng. 26(3), 698–710 (2014)
Hoi, S.C., Wang, J., Zhao, P., Jin, R.: Online feature selection for mining big data. In: Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, pp. 93–100 (2012)
Aridhi, S., d’Orazio, L., Maddouri, M., Nguifo, E.M.: Density based data partitioning strategy to approximate large-scale subgraph mining. Inf. Syst. 48, 213–223 (2015)
Qiu, H., Gu, R., Yuan, C., Huang, Y.: Yafim: a parallel frequent itemset mining algorithm with spark. In: IEEE International Parallel & Distributed Processing Symposium Workshops (IPDPSW), pp. 1664–1671 (2014)
Cheng, H., Yan, X., Han, J., Hsu, C.W.: Discriminative frequent pattern analysis for effective classification. In: International Conference on Data Engineering, pp. 716–725 (2007)
Cheng, H., Yan, X., Han, J., Yu, P.S.: Direct discriminative pattern mining for effective classification. In: Proceedings of ICDM ’08. IEEE Computer Society, Washington, DC, pp. 169–178 (2008)
Wu, X., Fan, W., Peng, J., Zhang, K., Yu, Y.: Iterative sampling based frequent itemset mining for big data. Int. J. Mach. Learn. Cybern. 6(6), 875–882 (2015)
Gole, S., Tidke, B.: ClustBIGFIM-frequent itemset mining of big data using pre-processing based on mapreduce framework. Int. J. Found. Comput. Sci. Technol. 5(3), 79–89 (2015)
Gawwad, M.A., Ahmed, M.F., Fayek, M.B.: Frequent itemset mining for big data using greatest common divisor technique. Data Sci. J. 16(25), 1–10 (2017)
Hasançebi, O., Teke, T., Pekcan, O.: A bat-inspired algorithm for structural optimization. Comput. Struct. 128, 77–90 (2013)
Xie, J., Zhou, Y., Chen, H.: A novel bat algorithm based on differential operator and Lévy flights trajectory. Comput. Intell. Neurosci. 2013, 1–13 (2013)
Yilmaz, S., Küçüksille, E.U.: A new modification approach on bat algorithm for solving optimization problems. Appl. Soft Comput. 28, 259–275 (2015)
Yang, X.-S., Deb, S.: Eagle strategy using Lévy walk and firefly algorithms for stochastic optimization. Stud. Comput. Intell. 284, 101–111 (2010)
Mantegna, R.N.: Fast, accurate algorithm for numerical simulation of Lévy stable stochastic processes. Phys. Rev. E 49(5), 4677–4683 (1994)
Schölkopf, B., Smola, A.J.: Learning with Kernels. MIT Press, Cambridge, MA (2002)
Cao, H., Naito, T., Ninomiya, Y.: Approximate RBF kernel SVM and its applications in pedestrian classification. In: The 1st International Workshop on Machine Learning for Vision-Based Motion Analysis-MLVMA’08 (2008)
Yekkehkhany, B., Safari, A., Homayouni, S., Hasanlou, M.: A comparison study of different Kernel functions for SVM-based classification of multi-temporal polarimetry SAR data. Int. Arch. Photogramm. Remote Sens. Spat. Inform. Sci. 40(2), 281–285 (2014)
Lanckriet, G., De Bie, T., Cristianini, N., Jordan, M.I., Stafford, Noble W.: A statistical framework for genomic data fusion. Bioinfomatics 20(16), 2626–2635 (2004)
Tsochantaridis, I., Hoffmann, T., Joachims, T., Altun, Y.: Support vector machine learning for interdependent and sturcutured output spaces. In: Proceedings of the 16th International Conference on Machine Learning (2004)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27–27 (2011)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Devi, S.G., Sabrigiriraj, M. Swarm intelligent based online feature selection (OFS) and weighted entropy frequent pattern mining (WEFPM) algorithm for big data analysis. Cluster Comput 22 (Suppl 5), 11791–11803 (2019). https://doi.org/10.1007/s10586-017-1489-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-017-1489-9