Abstract
Mining colossal itemsets have gained more attention in recent times. An extensive set of short and average sized itemsets do not confine complete and valuable information for decision making. But, the traditional itemset mining algorithms expend a gigantic measure of time in mining these little and average sized itemsets. Colossal itemsets are very significant for numerous applications including the field of bioinformatics and are influential during the decision making. The new mode of dataset known as long biological dataset was contributed by Bioinformatics. These datasets are high dimensional datasets, which are depicted by an expansive number of features (attributes) and a less number of rows (samples). Extracting huge amount of information and knowledge from high dimensional long biological dataset is a nontrivial task. The existing algorithms are computationally expensive and sequential in mining significant Frequent Colossal Closed itemsets (FCCI) from long biological dataset. Distributed computing is a good strategy to overcome the inefficiency of the existing sequential algorithm. The paper proposes a distributed computing approach for mining FCCI. The row enumerated mining search space is efficiently cut down by pruning strategy enclosed in Distributed Row Enumerated Frequent Colossal Closed Itemset Mining (DREFCCIM) algorithm. The proposed DREFCCIM algorithm is the first distributed algorithm to mine FCCI from long biological dataset. The experimental results demonstrate the efficient performance of the DREFCCIM algorithm in comparison to the current algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alves, R., Rodriguez-Baena, D.S., Aguilar-Ruiz, J.S.: Gene association analysis: a survey of frequent pattern mining from gene expression data. Briefings Bioinform. 11, 210–224 (2009)
Biological-Datasets. http://datam.i2r.a-star.edu.sg/datasets/krbd/index.html
Djenouri, Y., Djenouri, D., Belhadi, A., Cano, A.: Exploiting GPU and cluster parallelism in single scan frequent itemset mining. Inf. Sci. (2018)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. ACM Sigmod Rec. 29, 1–12 (2000)
Javed, A., Khokhar, A.: Frequent pattern mining on message passing multiprocessor systems. Distrib. Parallel Databases 16(3), 321–334 (2004)
Lin, K.C., Liao, I.E., Chang, T.P., Lin, S.F.: A frequent itemset mining algorithm based on the principle of inclusion-exclusion and transaction mapping. Inf. Sci. 276, 278–289 (2014)
Liu, H., Han, J., Xin, D., Shao, Z.: Mining frequent patterns on very high dimensional data: a topdown row enumeration approach. In: Proceeding of the 2006 SIAM International Conference on Data Mining (SDM 2006), Bethesda, MD, pp. 280–291. SIAM (2006)
Liu, H., Wang, X., He, J., Han, J., Xin, D., Shao, Z.: Top-down mining of frequent closed patterns from very high dimensional data. Inf. Sci. 179(7), 899–924 (2009)
Lucchese, C., Orlando, S., Perego, R.: Parallel mining of frequent closed patterns: harnessing modern computer architectures. In: Seventh IEEE International Conference on Data Mining, ICDM 2007, pp. 242–251. IEEE (2007)
Naulaerts, S., Meysman, P., Bittremieux, W., Vu, T.N., Berghe, W.V., Goethals, B., Laukens, K.: A primer to frequent itemset mining for bioinformatics. Briefings Bioinform. 16(2), 216–231 (2015)
Negrevergne, B., Termier, A., Méhaut, J.F., Uno, T.: Discovering closed frequent itemsets on multicore: parallelizing computations and optimizing memory accesses. In: 2010 International Conference on High Performance Computing and Simulation (HPCS), pp. 521–528. IEEE (2010)
Negrevergne, B., Termier, A., Rousset, M.C., Méhaut, J.F.: Para miner: a generic pattern mining algorithm for multi-core architectures. Data Min. Knowl. Discov. 28(3), 593–633 (2014)
Pan, F., Tung, A.K., Cong, G., Xu, X.: Cobbler: combining column and row enumeration for closed pattern discovery. In: 16th International Conference on Scientific and Statistical Database Management, Proceedings, pp. 21–30. IEEE (2004)
Sohrabi, M.K., Barforoush, A.A.: Efficient colossal pattern mining in high dimensional datasets. Knowl.-Based Syst. 33, 41–52 (2012)
Song, W., Yang, B., Xu, Z.: Index-BitTableFI: an improved algorithm for mining frequent itemsets. Knowl.-Based Syst. 21(6), 507–513 (2008)
Tanbeer, S.K., Ahmed, C.F., Jeong, B.S., Lee, Y.K.: Efficient single-pass frequent pattern mining using a prefix-tree. Inf. Sci. 179(5), 559–583 (2009)
Vo, B., Hong, T.P., Le, B.: DBV-miner: a dynamic bit-vector approach for fast mining frequent closed itemsets. Expert Syst. Appl. 39(8), 7196–7206 (2012)
Wang, J., Han, J., Pei, J.: Closet+: searching for the best strategies for mining frequent closed itemsets. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 236–245. ACM (2003)
Xun, Y., Zhang, J., Qin, X.: Fidoop: parallel mining of frequent itemsets using mapreduce. IEEE Trans. Syst. Man Cybern. Syst. 46(3), 313–325 (2016)
Yu, K.M., Zhou, J.: Parallel TID-based frequent pattern mining algorithm on a PC cluster and grid computing system. Expert Syst. Appl. 37(3), 2486–2494 (2010)
Zaki, M.J., Hsiao, C.J.: Efficient algorithms for mining closed itemsets and their lattice structure. IEEE Trans. Knowl. Data Eng. 17(4), 462–478 (2005)
Zhu, F., Yan, X., Han, J., Yu, P.S., Cheng, H.: Mining colossal frequent patterns by core pattern fusion. In: IEEE 23rd International Conference on Data Engineering, ICDE 2007, pp. 706–715. IEEE (2007)
Zulkurnain, N.F., Haglin, D.J., Keane, J.A.: Disclose: discovering colossal closed itemsets via a memory efficient compact row-tree. In: Emerging Trends in Knowledge Discovery and Data Mining, pp. 141–156. Springer (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Vanahalli, M.K., Patil, N. (2020). Distributed Mining of Significant Frequent Colossal Closed Itemsets from Long Biological Dataset. In: Abraham, A., Cherukuri, A.K., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-16657-1_83
Download citation
DOI: https://doi.org/10.1007/978-3-030-16657-1_83
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-16656-4
Online ISBN: 978-3-030-16657-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)