Threshold based Support Vector Machine Learning Algorithm for Sequential Patterns
Keywords:Data mining, Sequence Patterns, Threshold based Support Vector Machine Learning Algorithm, Classification Accuracy, Sequential mining
Now a days the pattern recognition is the major challenge in the field of data mining. The researchers focus on using data mining for wide variety of applications like market basket analysis, advertisement, and medical field etc., Here the transcriptional database is used for all the conventional algorithms, which is based on daily usage of object and/or performance of patients. Here the proposed research work uses sequential pattern mining approach using classification technique of Threshold based Support Vector Machine learning (T-SVM) algorithm. The pattern mining is to give the variable according to the user’s interest by statistical model. Here this proposed research work is used to analysis the gene sequence datasets. Further, the T-SVM technique is used to classify the dataset based on sequential pattern mining approach. Especially, the threshold-based model is used for predicting the upcoming state of interest by sequential patterns. Because this makes deeper understanding about sequential input data and classify the result by providing threshold values. Therefore, the proposed method is efficient than the conventional method by getting the value of achievable classification accuracy, precision, False Positive rate, True Positive rate and it also reduces operating time. This proposed model is performed in MATLAB in the adaptation of 2018a.
 Dilhan Perera., Judy Kay., Irena Koprinska., Kalina Yacef., and Osmar Zaiane.,(2009). "Clustering and Sequential Pattern Mining of Online Collaborative Learning Data," IEEE Transactions on knowledge and Data Engineering, Vol. 21, No. 6, pp.759-772. https://doi.org/10.1109/TKDE.2008.138
 Chieh-Yuan Tsai., and Chih-Jung Chen. (2015). "A PSO-AB classifier for solving sequence classification problems," Applied Soft Computing, Vol. 27, pp. 11-27. https://doi.org/10.1016/j.asoc.2014.10.029
 Sathish Kumar S, and N.Duraipandian., (2012). "An Efficient Identification of Species from DNA Sequence: A Classification Technique by Integrating DM and ANN," International Journal of Advanced Computer Science and Applications, Vol. 3, No. 8, pp. 104-114. https://doi.org/10.14569/IJACSA.2012.030817
 Gyula Dorgo., and Janos Abonyi., (2018), "Sequence Mining based Alarm Suppression," IEEE Access, Vol. 6, pp. 15365-15379. https://doi.org/10.1109/ACCESS.2018.2797247
 Bao Huynh., Bay Vo., and Vaclav Snasel., (2017) "An Efficient Parallel Method for Mining Frequent Closed Sequential Patterns," IEEE Access, Vol. 5, pp. 17392-17402. https://doi.org/10.1109/ACCESS.2017.2739749
 Po-Ming Law., Zhincheng Liu., Sana Malik., and Rahul C. Basole., (2019) "MAQUI: Interweaving Queries and Pattern Mining for Recursive Event Sequence Exploration," IEEE Transactions on Visualization and Computer GraphicsVol. 25, No. 1, pp. 396-406. https://doi.org/10.1109/TVCG.2018.2864886
 Tiantian Xu., Tongxuan Li., and Xiangjun Dong., (2018) ".Efficient High Utility Negative Sequential Patterns Mining in Smart Campus," IEEE Access, Vol. 6, pp. 23839-23847. https://doi.org/10.1109/ACCESS.2018.2827167
 Jinsong Zhang., Yinglin Wang., Chao Zhang., and Yongyong Shi., (2016) "Mining Contiguous sequential Generators in Biological Sequences," IEEE Transacions on Computational Biology and Bioinformatics, Vol. 13, No. 5. https://doi.org/10.1109/TCBB.2015.2495132
 Zhongliang Fu., Zongshun Tian., Yanqing Xu., and Kaichun Zhou., (2017) "Mining Frequent Route patterns Based on Personal Trajectory Abstraction," IEEE Access, Vol. 5, pp. 11352-11363. https://doi.org/10.1109/ACCESS.2017.2712703
 Anuja Jain., Varsha Sharma., and Vivek Sharma., (2017) "Big Data mining using machine learning approaches for Hadoop with Weka distribution," International Journal of Computational Intelligence Research, Vol. 13, No. 8, pp. 2095-2111.
 Seema Sharma., Jitendra Agrawal., Shikha Agarwal., and Sanjeev Sharma.(2013) "Machine Learning Techniques for Data Mining: A Survey," IEEE International Conference on Computational Intelligence and Computing Research. https://doi.org/10.1109/ICCIC.2013.6724149
 Kapil Sharma., Ashok., and Harish Rohil.(2014). "A Study of Sequential Pattern Mining Techniques," International Journal of Engineering and Management Research, Vol. 4, No. 1, pp. 241-248.
 Sadok Rezig., Zied Achour, and Nidhal Rezg.(2018) "Using Data Mining Methods for Predicting Sequential Maintenance Activities," Applied Science, Vol. 8, pp. 1-13. https://doi.org/10.3390/app8112184
 S Rajasekaran., and L Arockiam., (2014) "Frequent contiguous Pattern Mining Algorithm for Biological Data Sequences," Inter. Journal of Computer Applications, Vol. 95, No. 14, pp. 15-20. https://doi.org/10.5120/16661-6646
 Sandeep R Suthar., Vipul K Dabhi., and Harshadkumar B Prajapati., (2017) "Machine Learning Techniques in Hadoop Environment: A Survey," IEEE International Conference on Innovations in Power and Advanced Computing Technologies. https://doi.org/10.1109/IPACT.2017.8245018
 Ya-Bo Liu, and Da-You Liu., "Mining Attributes Sequential Patterns for Error Identification in Data Set," IEEE International Conf. on Machine learning and Cybernetics, pp. 1931-1936
 Cheng Zhou., Boris Cule., and Bart Goethals., (2016) "Pattern based Sequence Classification," IEEE Transactionson Knowledge nd Data Engineering, Vol. 28, No. 5, pp. 1285-1298. https://doi.org/10.1109/TKDE.2015.2510010
 Qiuju Yang., Jimin Liang., Zejun Hu., and Heng Zhao., (2012) "Auroral Sequence Representation and Classification using Hidden Markov Model," IEEE Transactions on Geoscience and Remote Sensing, Vol. 50, No. 12, pp. 5049-5060. https://doi.org/10.1109/TGRS.2012.2195667
 K. S. M. Tozammel Hossain., Debprakash Patnaik., Srivatsan Laxman., Prateek Jain., and Chris Bailey-Kellogg., (2013) ."Improved Multiple Sequence Alignments using Coupled Pattern Mining ," IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 10, October 2013, No. 5, pp. 1098-1112,. https://doi.org/10.1109/TCBB.2013.36
 Youxi Wu., Yao Tong., Xingquan Zhu., and Xindong Wu., (2018) "NOSEP: Nonoverlapping Sequence Pattern Mining with Gap Constraints," IEEE Transactions on Cybernetics, Vol. 48, October 2018, No. 10, pp. 2809-2822. https://doi.org/10.1109/TCYB.2017.2750691
 Ronald A Skoog., Thomas C Banwell., Joel W Gannett., Sarry F Habiby., and Marcus Pang., (2006 )"Automatic Identification of Impairments using Support Vector Machine Pattern Classification on Eye Diagrams," IEEE Photonics Technology Letters, Vol. 18, No. 22, pp. 2398-2400,. https://doi.org/10.1109/LPT.2006.886146
 Siyabend Turgut., Mustafa Dagtekin., and Tolga Ensari., (2018), "Microarray breast cancer data classification using Machine Learning Methods," IEEE Electric Electronicxs, Computer Science, Biomedical Engineering. https://doi.org/10.1109/EBBT.2018.8391468
 Christopher M. Bishop., (2006) "Pattern Recognition and Machine Learning," , pp. 1-758, Springer.
 Peter Wlodarczak., Jeffrey Soar., and Mustafa Ally., (2006)"Multimedia data mining using deep learning," IEEE Inter. Conf. on Digital Information Processing and Communications.
 Jian Pie., Jiawei Han., B Mortazavi-Asl., Jianyong Wang., H Pinto., Qiming Chen., U Dayal., and Mei-Chun Hsu, (2004) "Mining Sequential Patterns by Pattern-growth: the PrefixSpan Approach, IEEE Transactions on Knowledge and Data Engineering, Vol. 16, Issue 11, pp. 1424-1440. https://doi.org/10.1109/TKDE.2004.77
 Dhyanesh K. Parmar., Yagnik A. Rathod., and Mukesh M. Patel., (2014) "Survey on high utility oriented sequential pattern mining," IEEE International conference on Computational Intelligence and Computing Research. https://doi.org/10.1109/ICCIC.2013.6724204
 D Martens., B B Baesens., T Van Gestel., (2009), "Decompositional Rule Extraction from Support Vector Machines by Active Learning," IEEE Transactions on Knowledge and Data Engineering, Vol. 21, Issue 2, pp. 178-191. https://doi.org/10.1109/TKDE.2008.131
 Solomon H Ebenuwa., Mhd Saeed Sharif., Mamoun Alazab., Ameer AI-Nemrat., (2019). "Variance Ranking Attributes Selection Techniques for Binary Classification Probem in Imbalance Data," IEEE Access,24649-24666. https://doi.org/10.1109/ACCESS.2019.2899578
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