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ELP-M2: An Efficient Model for Mining Least Patterns from Data Repository

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Recent Advances on Soft Computing and Data Mining (SCDM 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 549))

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Abstract

Most of the algorithm and data structure facing a computational problem when they are required to deal with a highly sparse and dense dataset. Therefore, in this paper we proposed a complete model for mining least patterns known as Efficient Least Pattern Mining Model (ELP-M2) with LP-Tree data structure and LP-Growth algorithm. The comparative study is made with the well-know LP-Tree data structure and LP-Growth algorithm. Two benchmarked datasets from FIMI repository called Kosarak and T40I10D100K were employed. The experimental results with the first and second datasets show that the LP-Growth algorithm is more efficient and outperformed the FP-Growth algorithm at 14% and 57%, respectively.

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References

  1. Fayyad, U., Patesesky-Shapiro, G., Smyth, P., Uthurusamy, R.: Advances in Knowledge Discovery and Data Mining. MIT Press, Cambridge (1996)

    Google Scholar 

  2. Agrawal, R., Imielinski, T., Swami, A.: Database mining: a performance perspective. IEEE Trans. Knowl. Data Eng. 5(6), 914–925 (1993)

    Article  Google Scholar 

  3. Hipp, J., Guntzer, U. Nakhaeizadeh, G.: Algorithms for association rule mining – a general survey and comparison. In: Proceedings of SIGKDD Explorations. ACM, New York (2000). ACM SIGKDD 2(1), 58–64

    Google Scholar 

  4. Zhou, L., Yau, S.: Association rule and quantitative association rule mining among infrequent items. In: Koh, Y.S., Rountree, N. (eds.) Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection, pp. 15–32. IFI-Global, Pennsylvania (2010)

    Chapter  Google Scholar 

  5. Tan, P.-N., Steinbach, M., Kumar, V.: Introduction in Data Mining. Addison Wesley, Boston (2006)

    Google Scholar 

  6. Dunham, M.H.: Data Mining: Introductory and Advanced Topics. Prentice-Hall, New Jersey (2003)

    Google Scholar 

  7. Han, J., Kamber, M.: Data Mining Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)

    MATH  Google Scholar 

  8. Abdullah, Z., Herawan, T., Noraziah, A., Deris, M.M.: Extracting highly positive association rules from students’ enrollment data. Procedia Soc. Behav. Sci. 28, 107–111 (2011)

    Article  Google Scholar 

  9. Abdullah, Z., Herawan, T., Noraziah, A., Deris, M.M.: Mining significant association rules from educational data using critical relative support approach. Procedia Soc. Behav. Sci. 28, 97–101 (2011)

    Article  Google Scholar 

  10. Yun, U., Ryu, K.H.: Approximate weighted frequent pattern mining with/without noisy environments. Knowl. Based Syst. 24, 73–82 (2011)

    Article  Google Scholar 

  11. Duraiswamy, K., Jayanthi, B.: A novel preprocessing algorithm for frequent pattern mining in multidatasets. Int. J. Data Eng. (IJDE) 2(3), 111–118 (2011)

    Google Scholar 

  12. Leung, C.K.-S., Jiang, F.: Frequent pattern mining from time-fading streams of uncertain data. In: Cuzzocrea, A., Dayal, U. (eds.) DaWaK 2011. LNCS, vol. 6862, pp. 252–264. Springer, Heidelberg (2011). doi:10.1007/978-3-642-23544-3_19

    Chapter  Google Scholar 

  13. Leung, C.K.-S., Jiang, F., Hayduk, Y.: A landmark-model based system for mining frequent patterns from uncertain data streams. In: Proceedings IDEAS 2011, pp. 249–250. ACM Press (2011)

    Google Scholar 

  14. Abdullah, Z., Herawan, T., Deris, M.M.: Scalable model for mining critical least association rules. In: Zhu, R., Zhang, Y., Liu, B., Liu, C. (eds.) ICICA 2010. LNCS, vol. 6377, pp. 509–516. Springer, Heidelberg (2010). doi:10.1007/978-3-642-16167-4_65

    Chapter  Google Scholar 

  15. Abdullah, Z., Herawan, T., Deris, M.M.: Mining significant least association rules using fast SLP-growth algorithm. In: Kim, T.-h., Adeli, H. (eds.) ACN/AST/ISA/UCMA -2010. LNCS, vol. 6059, pp. 324–336. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13577-4_28

    Chapter  Google Scholar 

  16. Abdullah, Z., Herawan, T., Mat Deris, M.: An alternative measure for mining weighted least association rule and its framework. In: Zain, J.M., Wan Mohd, W., El-Qawasmeh, E. (eds.) ICSECS 2011. CCIS, vol. 180, pp. 480–494. Springer, Heidelberg (2011). doi:10.1007/978-3-642-22191-0_42

    Chapter  Google Scholar 

  17. Abdullah, Z., Herawan, T., Deris, M.M.: Visualizing the construction of incremental disorder trie itemset data structure (DOSTrieIT) for frequent pattern tree (FP-Tree). In: Badioze Zaman, H., Robinson, P., Petrou, M., Olivier, P., Shih, Timothy, K., Velastin, S., Nyström, I. (eds.) IVIC 2011. LNCS, vol. 7066, pp. 183–195. Springer, Heidelberg (2011). doi:10.1007/978-3-642-25191-7_18

    Chapter  Google Scholar 

  18. Herawan, T., Yanto, I.T.R., Mat Deris, M.: Soft set approach for maximal association rules mining. In: Ślęzak, D., Kim, T.-h., Zhang, Y., Ma, J., Chung, K.-i. (eds.) DTA 2009. CCIS, vol. 64, pp. 163–170. Springer, Heidelberg (2009). doi:10.1007/978-3-642-10583-8_19

    Chapter  Google Scholar 

  19. Herawan, T., Yanto, I.T.R., Deris, M.M.: SMARViz: soft maximal association rules visualization. In: Badioze Zaman, H., Robinson, P., Petrou, M., Olivier, P., Schröder, H., Shih, T.K. (eds.) IVIC 2009. LNCS, vol. 5857, pp. 664–674. Springer, Heidelberg (2009). doi:10.1007/978-3-642-05036-7_63

    Chapter  Google Scholar 

  20. Herawan, T., Deris, M.M.: A soft set approach for association rules mining. Knowl. Based Syst. 24(1), 186–195 (2011)

    Article  Google Scholar 

  21. Herawan, T., Vitasari, P., Abdullah, Z.: Mining interesting association rules of student suffering mathematics anxiety. In: Zain, J.M., Wan Mohd, W., El-Qawasmeh, E. (eds.) ICSECS 2011. CCIS, vol. 180, pp. 495–508. Springer, Heidelberg (2011). doi:10.1007/978-3-642-22191-0_43

    Chapter  Google Scholar 

  22. Abdullah, Z., Herawan, T., Deris, M.M.: Efficient and scalable model for mining critical least association rules. J. Chin. Inst. Eng. 35(4), 547–554 (2012). In a special issue from AST/UCMA/ISA/ACN 2010

    Article  Google Scholar 

  23. Herawan, T., Noraziah, A., Abdullah, Z., Deris, M.M., Abawajy, J.H.: IPMA: indirect patterns mining algorithm. In: Nguyen, N.-T., Hoang, K., Jȩdrzejowicz, P. (eds.) ICCCI 2012. SCI, vol. 457, pp. 187–196. Springer, Heidelberg (2012). doi:10.1007/978-3-642-34300-1_18

    Chapter  Google Scholar 

  24. Herawan, T., Noraziah, A., Abdullah, Z., Deris, M.M., Abawajy, J.H.: EFP-M2: efficient model for mining frequent patterns in transactional database. In: Nguyen, N.-T., Hoang, K., Jȩdrzejowicz, P. (eds.) ICCCI 2012. LNCS (LNAI), vol. 7654, pp. 29–38. Springer, Heidelberg (2012). doi:10.1007/978-3-642-34707-8_4

    Chapter  Google Scholar 

  25. Noraziah, A., Abdullah, Z., Herawan, T., Deris, M.M.: Scalable technique to discover items support from trie data structure. In: Liu, B., Ma, M., Chang, J. (eds.) ICICA 2012. LNCS, vol. 7473, pp. 500–507. Springer, Heidelberg (2012). doi:10.1007/978-3-642-34062-8_65

    Chapter  Google Scholar 

  26. Noraziah, A., Abdullah, Z., Herawan, T., Deris, M.M.: WLAR-Viz: weighted least association rules visualization. In: Liu, B., Ma, M., Chang, J. (eds.) ICICA 2012. LNCS, vol. 7473, pp. 592–599. Springer, Heidelberg (2012). doi:10.1007/978-3-642-34062-8_77

    Chapter  Google Scholar 

  27. Herawan, T., Abdullah, Z.: CNAR-M: a model for mining critical negative association rules. In: Li, Z., Li, X., Liu, Y., Cai, Z. (eds.) ISICA 2012. CCIS, vol. 316, pp. 170–179. Springer, Heidelberg (2012). doi:10.1007/978-3-642-34289-9_20

    Chapter  Google Scholar 

  28. Abdullah, Z., Herawan, T., Noraziah, A., Deris, M.M.: DFP-growth: an efficient algorithm for mining frequent patterns in dynamic database. In: Liu, B., Ma, M., Chang, J. (eds.) ICICA 2012. LNCS, vol. 7473, pp. 51–58. Springer, Heidelberg (2012). doi:10.1007/978-3-642-34062-8_7

    Chapter  Google Scholar 

  29. Khan, M.S., Muyeba, M.K., Coenen, F., Reid, D., Tawfik, H.: Finding associations in composite data sets: the Cfarm algorithm. Int. J. Data Warehous. Min. 7(3), 1–29 (2011)

    Article  Google Scholar 

  30. Koh, Y.S., Pears, R., Dobbie, G.: Automatic item weight generation for pattern mining and its application. Int. J. Data Warehous. Min. 7(3), 30–49 (2011)

    Article  Google Scholar 

  31. Jiang, B., Hu, X., Wei, Q., Song, J., Han, C., Liang, M.: Weak ratio rules: a generalized Boolean association rules. Int. J. Data Warehous. Min. 7(3), 50–87 (2011)

    Article  Google Scholar 

  32. Giannikopoulos, P., Varlamis, I., Eirinaki, M.: Mining frequent generalized patterns for web personalization in the presence of taxonomies. Int. J. Data Warehous. Min. 6(1), 58–76 (2010)

    Article  Google Scholar 

  33. Lu, J., Chen, W., Keech, M.: Graph-based modelling of concurrent sequential patterns. Int. J. Data Warehous. Min. 6(2), 41–58 (2010)

    Article  Google Scholar 

  34. Kiran, R.U., Reddy, P.K.: An improved frequent pattern-growth approach to discover rare association rules. In: Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (ICKDIR 2009), pp. 43–52. INSTICC Press, Funchal

    Google Scholar 

  35. Koh, Y.S., Rountree, N., O’keefe, R.A.: Finding non-coincidental sporadic rules using Apriori-inverse. Int. J. Data Warehous. Min. 2(2), 38–54 (2006)

    Article  Google Scholar 

  36. Ashrafi, M.Z., Taniar, D., Smith, K.A.: ODAM: an optimized distributed association rule mining algorithm. IEEE Distrib. Syst. Online 5(3), 1–18 (2004)

    Article  Google Scholar 

  37. Ashrafi, M.Z., Taniar, D., Smith, K.A.: Redundant association rules reduction techniques. Int. J. Bus. Intell. Data Min. 2(1), 29–63 (2007)

    MATH  Google Scholar 

  38. Taniar, D., Rahayu, W., Lee, V.C.S., Daly, O.: Exception rules in association rule mining. Appl. Math. Comput. 205(2), 735–750 (2008)

    MathSciNet  MATH  Google Scholar 

  39. Koh, Y.S., Rountree, N.: Finding sporadic rules using Apriori-inverse. In: Ho, T.B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 97–106. Springer, Heidelberg (2005). doi:10.1007/11430919_13

    Chapter  Google Scholar 

  40. Yun, H., Ha, D., Hwang, B., Ryu, K.H.: Mining association rules on significant rare data using relative support. J. Syst. Softw. 67(3), 181–191 (2003)

    Article  Google Scholar 

  41. Liu, B., Hsu, W., Ma, Y.: Mining association rules with multiple minimum support. In: Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 1999), pp. 337–341. ACM, San Diego (1999)

    Google Scholar 

  42. Wang, K., He, Y., Han, J.: Pushing support constraints into association rules mining. IEEE Trans. Knowl. Data Eng. 15(3), 642–658 (2003)

    Article  Google Scholar 

  43. Ding, J.: Efficient association rule mining among infrequent items. Ph.D. Thesis, University of Illinois at Chicago (2005)

    Google Scholar 

  44. Han, J., Pei, H., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of the 2000 ACM SIGMOD, pp. 1–12. ACM, Texas (2000)

    Google Scholar 

  45. Liu, G., Lu, H., Lou, W., Xu, Y., Yu, J.X.: Efficient mining of frequent patterns using ascending frequency ordered prefix-tree. Data Min. Knowl. Disc. 9, 249–274 (2004)

    Article  MathSciNet  Google Scholar 

  46. Koh, J.-L., Shieh, S.-F.: An efficient approach for maintaining association rules based on adjusting FP-Tree structures. In: Lee, Y., Li, J., Whang, K.-Y., Lee, D. (eds.) DASFAA 2004. LNCS, vol. 2973, pp. 417–424. Springer, Heidelberg (2004). doi:10.1007/978-3-540-24571-1_38

    Chapter  Google Scholar 

  47. Li, X., Deng, Z.-H., Tang, S.: A fast algorithm for maintenance of association rules in incremental databases. In: Li, X., Zaïane, O.R., Li, Z. (eds.) ADMA 2006. LNCS (LNAI), vol. 4093, pp. 56–63. Springer, Heidelberg (2006). doi:10.1007/11811305_5

    Chapter  Google Scholar 

  48. Cheung, W., and Zaïane, O.R.: Incremental Mining of Frequent Patterns without Candidate Generation of Support Constraint. In Proc. of the 7th International Database Engineering and Applications Symposium (IDEAS’03), IEEE Computer Society, New York, 111–117 (2003)

    Google Scholar 

  49. Hong, T.-P., Lin, J.-W., We, Y.-L.: Incrementally fast updated frequent pattern trees. Int. J. Expert Syst. Appl. 34(4), 2424–2435 (2008)

    Article  Google Scholar 

  50. Tanbeer, S.K., Ahmed, C.F., Jeong, B.-S., Lee, Y.-K.: CP-tree: a tree structure for single-pass frequent pattern mining. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 1022–1027. Springer, Heidelberg (2008). doi:10.1007/978-3-540-68125-0_108

    Chapter  Google Scholar 

  51. Totad, S.G., Geeta, R.B., Reddy, P.P.: Batch processing for incremental FP-Tree construction. Int. J. Comput. Appl. 5(5), 28–32 (2010)

    Google Scholar 

  52. Frequent Itemset Mining Dataset Repository. http://fimi.cs.helsinki.fi/data/

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Acknowledgement

This work is supported by the research grant from Research Acceleration Center Excellence (RACE) of Universiti Kebangsaan Malaysia.

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Authors and Affiliations

  1. Faculty of Entrepreneurship and Business / Centre of Computing and Informatics, Universiti Malaysia Kelantan, 16100, Kota Bharu, Kelantan, Malaysia

    Zailani Abdullah

  2. School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, 21030, Kuala Terengganu, Malaysia

    Amir Ngah

  3. Faculty of Computer Science and Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia

    Tutut Herawan

  4. Faculty of Computer System and Software Engineering, Universiti Malaysia Pahang Malaysia, 26300, Kuantan, Pahang, Malaysia

    Noraziah Ahmad

  5. Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia

    Siti Zaharah Mohamad & Abdul Razak Hamdan

Authors
  1. Zailani Abdullah
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  2. Amir Ngah
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  3. Tutut Herawan
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  4. Noraziah Ahmad
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  5. Siti Zaharah Mohamad
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  6. Abdul Razak Hamdan
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Correspondence to Zailani Abdullah .

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Editors and Affiliations

  1. Department of Information System, University of Malaya, Kuala Lumpur, Malaysia

    Tutut Herawan

  2. Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Rozaida Ghazali

  3. Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Nazri Mohd Nawi

  4. Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Mustafa Mat Deris

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Abdullah, Z., Ngah, A., Herawan, T., Ahmad, N., Mohamad, S.Z., Hamdan, A.R. (2017). ELP-M2: An Efficient Model for Mining Least Patterns from Data Repository. In: Herawan, T., Ghazali, R., Nawi, N.M., Deris, M.M. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2016. Advances in Intelligent Systems and Computing, vol 549. Springer, Cham. https://doi.org/10.1007/978-3-319-51281-5_23

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