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Fuzzy Statistical Decision-Making Read chapter Read first chapter

2016 | OriginalPaper | Chapter

A Survey of Fuzzy Data Mining Techniques

Authors : Tzung-Pei Hong, Chun-Hao Chen, Jerry Chun-Wei Lin

Published in: Fuzzy Statistical Decision-Making

Publisher: Springer International Publishing

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Abstract

Data mining is very popular recently due to lots of analysis applications of big data. A well-known algorithm for mining association rules from transactions is the Apriori algorithm. Because transactions may include quantitative values, fuzzy sets which can be used to handle quantitative values are thus utilized to mine fuzzy association rules. Hence in this chapter, some useful fuzzy data mining techniques are introduced. Firstly, with the predefined membership functions, the Apriori-based fuzzy data mining algorithms that provide an easily way to mine fuzzy association rules are described. Since they may be time-consuming when dataset size is large, several tree-based fuzzy data mining methods are then stated to improve the mining efficiency. Besides, how to define appropriate membership functions for fuzzy data mining is important and it can be transferred into an optimization problem. Four types of genetic-fuzzy mining approaches are thus given to find both membership functions and fuzzy association rules. At last, some extended issues are discussed to provide future research directions.

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previous chapter A Practical Application of Fuzzy Analysis of Variance in Agriculture
Literature
1.
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: The International Conference on Very Large Data Bases, pp. 487–499 (1994)
2.
Agrawal, R., Imielinski, T., Swami, A.: Database mining: A performance perspective. IEEE Trans. Knowl. Data Eng. 5, 914–925 (1993)CrossRef
3.
Agrawal, R., Srikant, R., Vu, Q.: Mining association rules with item constraints. In: The Third International Conference on Knowledge Discovery in Databases and Data Mining (1997)
4.
Alcala-Fdez, J., Alcala, R., Gacto, M.J., Herrera, F.: Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms. Fuzzy Sets Syst. 160(7), 905–921 (2009)MathSciNetCrossRefMATH
5.
Alhajj, R., Kaya, M.: Multi-objective genetic algorithms based automated clustering for fuzzy association rules mining. J. Intell. Inf. Syst. 31(3), 243–264 (2008)CrossRef
6.
Au, W.H., Chan, K.C.C.: Mining fuzzy association rules in a bank-account database. IEEE Trans. Fuzzy Syst. 11(2), 238–248 (2003)CrossRef
7.
Ayouni, S., Yahia, S.B.: Extracting compact and information lossless set of fuzzy association rules. In: IEEE International Fuzzy Systems Conference, pp. 1–6 (2007)
8.
Chan, K.C.C., Au, W.H.: Mining fuzzy association rules. In: International Conference on Information and Knowledge Management, pp. 209–215 (1997)
9.
Chan, R., Yang, Q., Shen, Y.D.: Mining high utility itemsets. In: The Third IEEE International Conference on Data Mining, pp. 19–26 (2003)
10.
Chen, M.S., Han, J., Yu, P.S.: Data mining: An overview from a database perspective. IEEE Trans. Knowl. Data Eng. 8, 866–883 (1996)CrossRef
11.
12.
Chen, C.H., Hong, T.P., Tseng, V.S.: A cluster-based fuzzy-genetic mining approach for association rules and membership functions. In: The IEEE International Conference on Fuzzy Systems, pp. 1411–1416 (2006)
13.
Chen, C.H., Hong, T.P., Tseng, V.S.: A modified approach to speed up genetic-fuzzy data mining with divide-and-conquer strategy. In: The IEEE Congress on Evolutionary Computation, pp. 1–6 (2007)
14.
Chen, C.H., Hong, T.P., Tseng, V.S., Chen, L.C.: A multi-objective genetic-fuzzy mining algorithm. In: The IEEE International Conference on Granular Computing (2008)
15.
Chen, C.H., Hong, T.P., Tseng, V.S.:A cluster-based genetic-fuzzy mining approach for items with multiple minimum supports. Lect Notes Comput. Sci. 5012, 864–869 (2008)
16.
Chen, C.H., Hong, T.P., Tseng, V.S., Lee, C.S.: A genetic-fuzzy mining approach for items with multiple minimum supports. Soft Comput. 13(5), 521–533 (2009)
17.
Chen, C.H., Hong, T.P., Tseng, V.S.: An improved approach to find membership functions and multiple minimum supports in fuzzy data mining. Expert Syst. Appl. 36(6), 10016–10024 (2009)
18.
Chen, C.H., Hong, T.P., Tseng, V.S.: A SPEA2-based genetic-fuzzy mining algorithm. In: The IEEE International Conference on Fuzzy Systems (2010)
19.
Chen, C.H., Hong, T.P., Tseng, V.S.: Genetic-fuzzy mining with multiple minimum supports based on fuzzy clustering. Soft Comput. 15(12), 2319–2333 (2011)CrossRef
20.
Chen, C.H., Hong, T.P., Tseng, V.S.: Finding Pareto-front membership functions in fuzzy data mining. Int. J. Comput. Intell. Syst. 5(2), 343–354 (2012)CrossRef
21.
Chen, C.H., Hong, T.P., Lee, Y.C.: Genetic-fuzzy mining with taxonomy. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 20(2), 187–205 (2012)CrossRef
22.
Chen, C.H., He, J.S., Hong, T.P.: MOGA-based fuzzy data mining with taxonomy. Knowl. Based Syst. 54, 53–65 (2013)CrossRef
23.
Delgado, M., Marin, N., Sanchez, D., Vila, M.A.: Fuzzy association rules: general model and applications. IEEE Trans. Fuzzy Syst. 11(2), 214–225 (2003)CrossRef
24.
Dubois, D., Prade, H., Sudkamp, T.: On the representation, measurement, and discovery of fuzzy associations. IEEE Trans. Fuzzy Syst. 13(2), 250–262 (2005)CrossRef
25.
Farzanyar, Z., kangavari, M., Hashemi, S.: A new algorithm for mining fuzzy association rules in the large databases based on ontology. In: IEEE International Conference on Data Mining Workshops, pp. 65–69 (2006)
26.
Gautam, P., Khare, N., Pardasani, K.R.: A model for mining multilevel fuzzy association rule in database. CoRR abs/1001.3488 (20100
27.
Gyenesei, A.: A fuzzy approach for mining quantitative association rules. Acta Cybern. 15, 305–320 (2001)MathSciNetMATH
28.
Han, J., Fu, Y.: Discovery of multiple-level association rules from large database. In: The Twenty-first International Conference on Very Large Data Bases, pp. 420–431 (1995)
29.
Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min. Knowl. Disc. 8, 53–87 (2004)MathSciNetCrossRef
30.
Hong, T.P.: A study of parallel processing and noise management on machine learning. Ph.D. dissertation, National Chiao-Tung University, Taiwan, R.O.C. (1992)
31.
Hong, T.P., Kuo, C.S., Chi, S.C.: Mining association rules from quantitative data. Intell. Data Anal. 3, 363–376 (1999)CrossRefMATH
32.
Hong, T.P., Chiang, M.J., Wang, S.L.: Mining from quantitative data with linguistic minimum supports and confidences. In: IEEE International Conference on Fuzzy Systems, pp. 494–499 (2002)
33.
Hong, T.P., Lin, K.Y., Chien, B.C.: Mining fuzzy multiple-level association rules from quantitative data. Appl. Intell. 18, 79–90 (2003)CrossRefMATH
34.
Hong, T.P., Chen, C.H., Wu, Y.L., Lee, Y.C.: Mining membership functions and fuzzy association rules. In: The Joint Conference on AI, Fuzzy System, and Grey System (2003)
35.
Hong, T.P., Chen, C.H., Wu, Y.L., Lee, Y.C.: Using divide-and-conquer GA strategy in fuzzy data mining. In: The Ninth IEEE Symposium on Computers and Communications (2004)
36.
Hong, T.P., Kuo, C.S., Wang, S.L.: A fuzzy aprioritid mining algorithm with reduced computational time. Appl. Soft Comput. 5, 1–10 (2004)CrossRef
37.
Hong, T.P., Chen, C.H., Wu, Y.L., Tseng, S.M.: Finding active membership functions in fuzzy data mining. In: The Workshop on Foundations of Data Mining in The Fourth IEEE International Conference on Data Mining (2004)
38.
Hong, T.P., Chen, C.H., Wu, Y.L., Lee, Y.C.: A GA-based fuzzy mining approach to achieve a trade-off between number of rules and suitability of membership functions. Soft Comput. 10(11), 1091–1101 (2006)CrossRef
39.
Hong, T.P., Chen, C.H., Lee, Y.C., Wu, Y.L.: Genetic-fuzzy data mining with divide-and-conquer strategy. IEEE Trans. Evol. Comput. 12(2), 252–265 (2008)CrossRef
40.
Hong, T.P., Lin, C.W., Lin, T.C., Wang, S.L.: Incremental multiple fuzzy frequent pattern tree. In: The IEEE International Conference on Fuzzy Systems, pp. 2051–2055 (2012)
41.
Hong, T.P., Lin, C.W., Lin, T.C.: The MFFP-tree fuzzy mining algorithm to discover complete linguistic frequent itemsets. Comput. Intell. 30, 145–166 (2012)MathSciNetCrossRef
42.
Hong, T.P., Lee, Y.C., Wu, M.T.: An effective parallel approach for genetic-fuzzy data mining. Expert Syst. Appl. 41(2), 655–662 (2014)CrossRef
43.
Janikow, C.Z.: Fuzzy decision trees: issues and methods. IEEE Trans. Syst. Man Cybern. B Cybern. 28, 1–14 (1998)CrossRef
44.
Joyce, S.Y., Tseng, E., Yeung, D., Shi, D.: Mining fuzzy association rules with weighted items. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 1906–1911 (2000)
45.
Kaya, M.: Multi-objective genetic algorithm based approaches for mining optimized fuzzy association rules. Soft Comput. 10(7), 578–586 (2006)MathSciNetCrossRefMATH
46.
Kaya, M., Alhajj, R.: Facilitating fuzzy association rules mining by using multi-objective genetic algorithms for automated clustering. In: IEEE International Conference on Data Mining, pp. 561–564 (2003)
47.
Kaya, M., Alhajj, R.: Genetic algorithms based optimization of membership functions for fuzzy weighted association rules mining. In: IEEE Symposium on Computers and Communications, pp. 110–115 (2004)
48.
Kaya, M., Alhajj, R.: Genetic algorithm based framework for mining fuzzy association rules. Fuzzy Sets Syst. 152(3), 587–601 (2005)MathSciNetCrossRefMATH
49.
Kaya, M., Alhajj, R.: Effective mining of fuzzy multi-cross-level weighted association rules. Lect. Notes Comput. Sci. 4203, 399–408 (2006)CrossRef
50.
Kuok, C.M., Fu, A.W.-C., Wong, M.H.: Mining fuzzy association rules in databases. ACM SIGMOD Rec. 27(1), 41–46 (1998)CrossRef
51.
Lan, G.C., Hong, T.P., Lin, Y.H., Wang, S.L.: Fuzzy utility mining with upper-bound measure. Appl. Soft Comput. 30, 767–777 (2015)CrossRef
52.
Lee, K.M.: Mining generalized fuzzy quantitative association rules with fuzzy generalization hierarchies. In: IFSA World Congress and 20th NAFIPS International Conference, vol. 5, pp. 2977–2982 (2001)
53.
Lee, Y.C., Hong, T.P., Lin, W.Y.: Mining fuzzy association rules with multiple minimum supports using maximum constraints. Lect. Notes Comput. Sci. 3214, 1283–1290 (2004)CrossRef
54.
Lee, Y.C., Hong, T.P., Wang, T.C.: Mining multiple-level association rules under the maximum constraint of multiple minimum supports. In: IEA/AIE, pp. 1329–1338 (2006)
55.
Lee, Y.C., Hong, T.P., Wang, T.C.: Multi-level fuzzy mining with multiple minimum supports. Expert Syst. Appl. 34(1), 459–468 (2008)CrossRef
56.
Lin, C.W., Hong, T.P.: A survey of fuzzy web mining. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 3, 190–199 (2013)CrossRef
57.
Lin, C.W., Hong, T.P.: Mining fuzzy frequent itemsets based on UBFFP trees. J. Intell. Fuzzy Syst. 27, 535–548 (2014)MathSciNet
58.
Lin, C.W., Hong, T.P., Lu, W.H.: Linguistic data mining with fuzzy FP-trees. Expert Syst. Appl. 37, 4560–4567 (2010)CrossRef
59.
Lin, C.W., Hong, T.P., Lu, W.H.: An efficient tree-based fuzzy data mining approach. Int. J. Fuzzy Syst. 12, 150–157 (2010)
60.
Lin, C.W., Hong, T.P., Lin, T.C., Chen, Y.F., Pan, S.T.: An integrated MFFP-tree algorithm for mining global fuzzy rules from distributed databases. J. Univ. Comput. Sci. 19(4), 521–538 (2013)
61.
Lin, C.W., Wu, T.Y., Lin, G., Hong, T.P.: Maintenance algorithm for updating the discovered multiple fuzzy frequent itemsets for transaction deletion. In: The International Conference on Machine Learning and Cybernetics, pp. 475–480 (2014) (China)
62.
Lin, J.C.W., Hong, T.P., Lin, T.C.: A cmffp-tree algorithm to mine complete multiple fuzzy frequent itemsets. Appl. Soft Comput. 28, 431–439 (2015)CrossRef
63.
Liu, B., Hsu, W., Ma, Y.: Mining association rules with multiple minimum supports. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 337–341 (1999)
64.
Lopez, F.J., Blanco, A., Garcia, F., Marin, A.: Extracting biological knowledge by fuzzy association rule Mining. In: IEEE International Fuzzy Systems Conference, pp. 1–6 (2007)
65.
Lui, C.L., Chung, F.L.: Discovery of generalized association rules with multiple minimum supports. Lect. Notes Comput. Sci. 1910, 510–515 (2000)CrossRef
66.
Mangalampalli, A., Pudi, V.: Fuzzy association rule mining algorithm for fast and efficient performance on very large datasets. In: IEEE International Conference on Fuzzy Systems, pp. 1163–1168 (2009)
67.
Matthews, S.G., Gongora, M.A., Hopgood, A.A.: Evolving temporal fuzzy itemsets from quantitative data with a multi-objective evolutionary algorithm. In: The IEEE International Workshop on Genetic and Evolutionary Fuzzy Systems (2011)
68.
Matthews, S.G., Gongora, M.A., Hopgood, A.A., Ahmadi, S.: Temporal fuzzy association rule mining with 2-tuple linguistic representation. In: The IEEE International Conference on Fuzzy Systems, pp. 1–8 (2012)
69.
Matthews, S.G., Gongora, M.A., Hopgood, A.A., Ahmadi, S.: Web usage mining with evolutionary extraction of temporal fuzzy association rules. Knowl. Based Syst. 54, 66–72 (2013)CrossRef
70.
71.
Ouyang, W., Huang, Q.: Mining direct and indirect weighted fuzzy association rules in large transaction databases. Int. Conf. Fuzzy Syst. and Knowl. Discov. 3, 128–132 (2009)
72.
Ouyang, W., Huang, Q., Luo, S.: Mining direct and indirect fuzzy multiple level sequential patterns in large transaction databases. In: ICIC, pp. 906–913 (2008)
73.
Papadimitriou, S., Mavroudi, S.: The frequent fuzzy pattern tree. In: The WSEAS International Conference on Computers (2005)
74.
Parodi, A., Bonelli, P.: A new approach of fuzzy classifier systems. In: Proceedings of Fifth International Conference on Genetic Algorithms, Morgan Kaufmann, Los Altos, CA, pp. 223–230 (1993)
75.
Srikant, R., Agrawal, R.: Mining generalized association rules. In: The Twenty-first International Conference on Very Large Data Bases, pp. 407–419 (1995)
76.
Thilagam, P.S., Ananthanarayana, V.S.: Extraction and optimization of fuzzy association rules using multi-objective genetic algorithm. Pattern Anal. Appl. 12(2), 159–168 (2008)MathSciNetCrossRef
77.
Tseng, M.C., Li, W.Y.: Efficient mining of generalized association rules with non-uniform minimum support. Data Knowl. Eng. 62(1), 41–64 (2007)MathSciNetCrossRef
78.
Wang, C.M., Chen, S.H., Huang, Y.F.: A fuzzy approach for mining high utility quantitative itemsets. In: The IEEE International Conference on Fuzzy Systems, pp. 20–24 (2009)
79.
Watanabe, T.: Mining fuzzy association rules of specified output field. IEEE Int. Conf. Syst. Man Cybern. 6, 5754–5759 (2004)
80.
81.
82.
Zaheeruddin, Anwer, J.: A simple technique for generation and minimization of fuzzy rules fuzzy systems. In: IEEE International Conference on Fuzzy Systems, pp. 489–494 (2005)
Metadata
Title
A Survey of Fuzzy Data Mining Techniques
Authors
Tzung-Pei Hong
Chun-Hao Chen
Jerry Chun-Wei Lin
Copyright Year
2016
Book
Fuzzy Statistical Decision-Making

Print ISBN: 978-3-319-39012-3

Electronic ISBN: 978-3-319-39014-7

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-39014-7_18

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