Textual outlier detection with an unsupervised method using text similarity and density peak

[1] F. Abedini, M. Bahaghighat, M. S’hoyan, Wind turbine tower detection using feature descriptors and deep learning. Facta Universitatis, Series: Electronics and Energetics, 33, 1 (2019) 133–153. ⇒105 Search in Google Scholar

[2] J. Allan, V. Lavrenko, D. Malin, R. Swan, Detections, bounds, and timelines: Umass and tdt-3. In Proceedings of Topic Detection and Tracking Workshop, pp. 167–174. Citeseer, 2000. ⇒92 Search in Google Scholar

[3] M. Bahaghighat, F. Abedini, Q: Xin, M. Mohammadi Zanjireh, S. Mirjalili, Using machine learning and computer vision to estimate the angular velocity of wind turbines in smart grids remotely. Energy Reports, 7 (2021) 8561–8576. ⇒92 Search in Google Scholar

[4] M. Bahaghighat, Q. Xin, S. Ahmad Motamedi, M. Mohammadi Zanjireh, A. Vacavant, Estimation of wind turbine angular velocity remotely found on video mining and convolutional neural network. Applied Sciences, 10, 10 (2020) 3544. ⇒105 Search in Google Scholar

[5] C. Barreyre, L. Boussouf, B. Cabon, B. Laurent, J-M. Loubes, Statistical methods for outlier detection in space telemetries. Space Operations: Inspiring Hu-mankind’s Future, pp. 513–547, 2019. ⇒93 Search in Google Scholar

[6] I. Ben-Gal, Outlier detection in: Data mining and knowledge discovery handbook: A complete guide for practitioners and researchers, 2005. ⇒93 Search in Google Scholar

[7] Y. Bengio, O. Delalleau, C. Simard, Decision trees do not generalize to new variations. Computational Intelligence, 26, 4 (2010) 449–467. ⇒100 Search in Google Scholar

[8] M. Bozorgi, M. Mohammadi Zanjireh, M. Bahaghighat, Q. Xin, A time-e cient and exploratory algorithm for the rectangle packing problem. Intelligent Automation & Soft Computing, 31, 2 (2022) 885–898. ⇒92 Search in Google Scholar

[9] A. Z. Broder, S. C. Glassman, M. S Manasse, G. Zweig, Syntactic clustering of the web. Computer networks and ISDN systems, 29, 8–13 (í997) 1157–1166. ⇒98 Search in Google Scholar

[10] M. Ester, H-P. Kriegel, J. Sander, X. Xu, et al., A density-based algorithm for discovering clusters in large spatial databases with noise. In kdd, vol. 96, pp. 226–231, 1996. ⇒93 Search in Google Scholar

[11] M. Ghorbani, M. Bahaghighat, Q. Xin, F.Özen, ConvLSTMconv network: a deep learning approach for sentiment analysis in cloud computing. Journal of Cloud Computing, 9, Article no: 16 (2020). ⇒92, 105 Search in Google Scholar

[12] J. Guzman, B. Poblete, On-line relevant anomaly detection in the twitter stream: an e cient bursty keyword detection model. In Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description, pp. 31–39, 2013. ⇒92, 94 Search in Google Scholar

[13] A. Hajikarimi, M. Bahaghighat, Optimum outlier detection in internet of things industries using autoencoder. In Frontiers in Nature-Inspired Industrial Optimization, pp. 77–92, 2022. ⇒92 Search in Google Scholar

[14] D. J. Higham, An algorithmic introduction to numerical simulation of stochastic differential equations. SIAM Review, 43, 3 (2001) 525–546. ⇒100 Search in Google Scholar

[15] T. K. Ho, Random decision forests. In Proc. of 3rd Int. Conf. on Document Analysis and Recognition, vol. 1. pp. 278–282. IEEE, 1995 ⇒99 Search in Google Scholar

[16] V. Hodge, J. Austin, A survey of outlier detection methodologies. Artificial Intelligence Review, 22 (2004) 85–126. ⇒92 Search in Google Scholar

[17] M. Jamalzadeh, M. Maadani, M. Mahdavi, Ec-mopso: an edge computing-assisted hybrid cluster and mopso-based routing protocol for the internet of vehicles. Annals of Telecommunications, 77, 7–8 (2022) 491–503. ⇒93 Search in Google Scholar

[18] S. M. Jameii, M. Maadani, Intelligent dynamic connectivity control algorithm for cluster-based wireless sensor networks. In 2016 11th Int. Conf. for Internet Technology and Secured Transactions (ICITST), pp. 416–420. IEEE, 2016. ⇒93 Search in Google Scholar

[19] T. Joachims, A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization. Technical Report, Carnegie-Mellon Univ. Pittsburgh. Dept. of Computer Science, 1996. ⇒98 Search in Google Scholar

[20] S. Kannan, V. Gurusamy, S. Vijayarani, J. Ilamathi, Ms. Nithya, S. Kannan, V. Gurusamy, Preprocessing techniques for text mining. International Journal of Computer Science & Communication Networks, 5, 1 (2014) 7–16. ⇒92 Search in Google Scholar

[21] F. Khorasani, M. Mohammadi Zanjireh, M. Bahaghighat, Q. Xin, A tradeo between accuracy and speed for k-means seed determination. Comput. Syst. Sci. Eng., 40, 3 (2022) 1085–1098. ⇒92 Search in Google Scholar

[22] B. S. Kumar, V. Ravi, A survey of the applications of text mining in financial domain. Knowledge-Based Systems, 114 (2016) 128–147. ⇒92 Search in Google Scholar

[23] R. Kumaraswamy, A. Wazalwar, T. Khot, J. Shavlik, S. Natarajan, Anomaly detection in text: The value of domain knowledge. In The Twenty-Eighth International Flairs Conference, 2015. ⇒92 Search in Google Scholar

[24] Y. Li, Z. Chen, D. Zha, K. Zhou, H. Jin, H. Chen, X. Hu. Autood: Automated outlier detection via curiosity-guided search and self-imitation learning. arXiv preprint arXiv:2006.11321, 2020. ⇒92 Search in Google Scholar

[25] Y. Liu, Z. Li, Ch. Zhou, Y. Jiang, J. Sun, M. Wang, X. He, Generative adversarial active learning for unsupervised outlier detection. IEEE Transactions on Knowledge and Data Engineering, 32, 8 (2019) 1517–1528. ⇒93 Search in Google Scholar

[26] A. R. Lubis, M. Lubis, et al., Optimization of distance formula in k-nearest neighbor method. Bulletin of Electrical Engineering and Informatics, 9, 1 (2020) 326–338. ⇒99 Search in Google Scholar

[27] H. P. Luhn, A statistical approach to mechanized encoding and searching of literary information. IBM Journal of Research and Development, 1, 4 (1957) 309–317. ⇒98 Search in Google Scholar

[28] M. Norouzi Shad, M. Maadani, M. Nesari Moghadam, Gapso-Svm: an IDSS-based energy-aware clustering routing algorithm for IoT perception layer. Wireless Personal Communications, 216 (2022) 2249–2268. ⇒93 Search in Google Scholar

[29] M. Oghbaie, M. Mohammadi Zanjireh, Pairwise document similarity measure based on present term set. Journal of Big Data, 5, 1 (2018) 1–23. ⇒98 Search in Google Scholar

[30] M. Platakis, D. Kotsakos, D. Gunopulos, Searching for events in the blogosphere. In Proceedings of the 18th Int. Conf. on World Wide Web, pp. 1225–1226, 2009. ⇒92 Search in Google Scholar

[31] X. Qin, L. Cao, E. A. Rundensteiner, S. Madden, Scalable kernel density estimation-based local outlier detection over large data streams. In Proceedings of the 22nd Int. Conf. on Extending Database Technology (EDBT), 2019. ⇒93 Search in Google Scholar

[32] J. P. Reiter, T. E. Raghunathan, The multiple adaptations of multiple imputation. Journal of the American Statistical Association, 102, 480 (2007) 1462–1471. ⇒99 Search in Google Scholar

[33] M. Rostami, M. Bahaghighat, M. Mohammadi Zanjireh, Bitcoin daily close price prediction using optimized grid search method. Acta Universitatis Sapientiae, Informatica, 13, 2 (2021) 265–287. ⇒92 Search in Google Scholar

[34] S. N. Sajedi, M. Maadani, M. Nesari Moghadam, F-leach: a fuzzy-based data aggregation scheme for healthcare IoT systems. The Journal of Supercomputing, 78, 1 (2022) 1030–1047. ⇒92 Search in Google Scholar

[35] E. Schubert, M. Weiler, H-P. Kriegel, Signitrend: scalable detection of emerging topics in textual streams by hashed significance thresholds. In Proceedings of the 20th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 871–880, 2014. ⇒92 Search in Google Scholar

[36] H. Schütze, Ch. D. Manning, P. Raghavan, Introduction to information retrieval, vol. 39. Cambridge University Press Cambridge, 2008. ⇒98 Search in Google Scholar

[37] A. Shamseen, M. Mohammadi Zanjireh, M. Bahaghighat, Q. Xin, Developing a parallel classifier for mining in big data sets. IIUM Engineering Journal, 22, 2 (2021) 119–134. ⇒92, 95 Search in Google Scholar

[38] M: Templ, J. Gussenbauer, P. Filzmoser, Evaluation of robust outlier detection methods for zero-inflated complex data. Journal of Applied Statistics, 47, 7 (2020) 1144–11673. ⇒92 Search in Google Scholar

[39] B. Wang, J. Sharma, J. Chen, P. Persaud, Ensemble machine learning assisted reservoir characterization using field production data–an o shore field case study. Energies, 14, 4 (2021) 1052. ⇒101 Search in Google Scholar

[40] Y. Wu, X. Li, F. Luan, Y. He, A novel gpr-based prediction model for strip crown in hot rolling by using the improved local outlier factor. IEEE Access, 9 (2020) 458–469. ⇒94 Search in Google Scholar

[41] Y. Yan, L. Cao, C. Kulhman, E. Rundensteiner, Distributed local outlier detection in big data. In Proceedings of the 23rd ACM SIGKDD Int. Conference on knowledge Discovery and Data Mining, pp. 1225–1234, 2017. ⇒92, 93 Search in Google Scholar

[42] Y. Zhao, Z. Nasrullah, Z. Li, PyOD: A Python toolbox for scalable outlier detection. arXiv preprint arXiv:1901.01588, 2019. ⇒92 Search in Google Scholar