Self-Explainable Reduction Method for Mixed Feature Data Modeling
Jiang Siwei1,2, Xie Zhenping1,2(), Chen Meijie1,2, Cai Ming3
1School of Digital Media, Jiangnan University, Wuxi 214122, China 2Jiangsu Key Laboratory of Media Design and Software Technology, Wuxi 214122, China 3Center of Informatization Development and Management, Jiangnan University, Wuxi 214122, China
[Objective] This paper aims to mine the data with continuous numeric and label features. [Methods] We proposed a self-explainable reduction model to represent the data. The proposed model used the new reduction objective to create adaptive discrete division for continuous data dimension. [Results] We examined the new model with standard datasets and found it had better performance than the existing ones. [Limitations] The computational efficiency of the proposed method was not very impressive, which cannot meet the demand of large-scale data mining. [Conclusions] The proposed model is innovative and practical to model the mixed feature data.
Agrawal R, Imieliński T, Swami A.Mining Association Rules Between Sets of Items in Large Databases[C]// Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data. ACM, 1993: 207-216.
[2]
Hsu C N, Huang H J, Wong T T.Why Discretization Works for Naive Bayesian Classifiers[C]// Proceedings of the 17th International Conference on Machine Learning. 2000: 399-406.
[3]
García S, Luengo J, Sáez J A, et al. A Survey of Discretization Techniques: Taxonomy and Empirical Analysis in Supervised Learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(4): 734-750.
doi: 10.1109/TKDE.2012.35
[4]
Mahanta P, Ahmed H A, Kalita J K, et al.Discretization in Gene Expression Data Analysis: A Selected Survey[C]// Proceedings of the 2nd International Conference on Computational Science, Engineering and Information Technology. 2011: 69-75.
[5]
Pearl J.Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference[J]. Computer Science Artificial Intelligence, 1988, 70(2): 1022-1027.
[6]
Waugh N T, Muir D D.Improving the Life Cycle Management of Power Transformers Transforming Data to Life[C]//Proceedings of the 2015 SoutheastCon. IEEE, 2015: 1-7.
[7]
Altaf W, Shahbaz M, Guergachi A.Applications of Association Rule Mining in Health Informatics: A Survey[J]. Artificial Intelligence Review, 2017, 47(3): 313-340.
doi: 10.1007/s10462-016-9483-9
(Lu Yonghe, Cao Lichao.Books Recommended Model Based on Association Rules Comprehensive Evaluation[J]. New Technology of Library and Information Service, 2011(2): 81-86.)
[10]
Agrawal B R, Srikant R.A Fast Algorithm for Mining Association Rules[C]//Proceedings of the 20th International Conference on Very Large Data Bases. 1994: 21-30.
[11]
Han J, Pei J, Yin Y.Mining Frequent Patterns Without Candidate Generation[J]. ACM SIGMOD Record, 2009, 29(2): 1-12.
[12]
Zaki M J.Scalable Algorithms for Association Mining[J]. IEEE Transactions on Knowledge and Data Engineering, 2000, 12(3): 372-390.
doi: 10.1109/69.846291
[13]
Qian G, Rao C R, Sun X, et al.Boosting Association Rule Mining in Large Datasets via Gibbs Sampling[J]. Proceedings of the National Academy of Sciences of the United States of America, 2016, 113(18): 4958-4963.
doi: 10.1073/pnas.1604553113
pmid: 27091963
[14]
Sheng G, Hou H, Jiang X, et al.A Novel Association Rule Mining Method of Big Data for Power Transformers State Parameters Based on Probabilistic Graph Model[J]. IEEE Transactions on Smart Grid, 2016(99): 1.
doi: 10.1109/TSG.2016.2562123
[15]
Li J, Le T D, Liu L, et al. From Observational Studies to Causal Rule Mining[J]. ACM Transactions on Intelligent Systems and Technology, 2016, 7(2): Article No. 14.
doi: 10.1145/2746410
[16]
Song K, Lee K.Predictability-based Collective Class Association Rule Mining[J]. Expert Systems with Applications, 2017, 79: 1-7.
doi: 10.1016/j.eswa.2017.02.024
[17]
Agbehadji I E, Fong S, Millham R.Wolf Search Algorithm for Numeric Association Rule Mining[C]//Proceedings of the 2016 IEEE International Conference on Cloud Computing and Big Data Analysis. IEEE, 2016: 146-151.
[18]
Jorge A M, Azevedo P J.Optimal Leverage Association Rules with Numerical Interval Conditions[J]. Intelligent Data Analysis, 2012, 16(1): 25-47.
doi: 10.3233/IDA-2011-0509
[19]
Rastogi R, Shim K.Mining Optimized Association Rules with Categorical and Numeric Attributes[J]. IEEE Transactions on Knowledge & Data Engineering, 2002, 14(1): 29-50.
doi: 10.1109/ICDE.1998.655813
[20]
Biba M, Esposito F, Ferilli S, et al.Unsupervised Discretization Using Kernel Density Estimation[C]// Proceedings of the 2017 International Joint Conference on Artificial Intelligence, Hyderabad, India. 2008: 696-701.
[21]
Schmidberger G, Frank E.Unsupervised Discretization Using Tree-based Density Estimation[C]//Proceedings of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal. 2005.
[22]
Shanmugapriya M, Nehemiah H K, Bhuvaneswaran R S, et al.Unsupervised Discretization: An Analysis of Classification Approaches for Clinical Datasets[J]. Research Journal of Applied Sciences Engineering & Technology, 2017, 14(2): 67-72.
doi: 10.19026/rjaset.14.3991
[23]
Paninski L.Estimation of Entropy and Mutual Information[J]. Neural Computation, 2006, 15(6): 1191-1253.
doi: 10.1162/089976603321780272
[24]
Ferguson T S.A Bayesian Analysis of Some Nonparametric Problems[J]. Annals of Statistics, 1973, 1(2): 209-230.
[25]
Teh Y W, Jordan M I, Beal M J, et al.Hierarchical Dirichlet Processes[J]. Journal of the American Statistical Association, 2006, 101(476): 1566-1581.
