The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Data mining: concepts and techniques
Abstract
No
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
Cited by
Machine learning: Algorithms, real-world applications and research directions
Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00592-x · Cited by 5,583
Data-intensive applications, challenges, techniques and technologies: A survey on Big Data
Philip Chen, C. L., & Zhang, C.-Y. (2014) · Information Sciences · doi:10.1016/j.ins.2014.01.015 · Cited by 3,047
Big Data, new epistemologies and paradigm shifts
Kitchin, R. (2014) · Big Data & Society · doi:10.1177/2053951714528481 · Cited by 2,355
Time-series clustering – A decade review
Aghabozorgi, S., Seyed Shirkhorshidi, A., & Ying Wah, T. (2015) · Information Systems · doi:10.1016/j.is.2015.04.007 · Cited by 1,826
Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation
Wong, T.-T. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.03.009 · Cited by 1,562
Machine learning and data mining methods in diabetes research
Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2016.12.005 · Cited by 1,445
Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011) · Decision Support Systems · doi:10.1016/j.dss.2010.08.006 · Cited by 1,361
Sarker, I. H. (2022) · SN Computer Science · doi:10.1007/s42979-022-01043-x · Cited by 1,208
Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.07.040 · Cited by 1,141
Toward Scalable Systems for Big Data Analytics: A Technology Tutorial
Hu, H., Wen, Y., Chua, T.-S., & Li, X. (2014) · IEEE Access · doi:10.1109/access.2014.2332453 · Cited by 1,099
An overview of online fake news: Characterization, detection, and discussion
Zhang, X., & Ghorbani, A. A. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2019.03.004 · Cited by 997
Industrial Product-Service Systems—IPS 2
Meier, H., Roy, R., & Seliger, G. (2010) · CIRP Annals · doi:10.1016/j.cirp.2010.05.004 · Cited by 994
Yeh, I.-C., & Lien, C.-H. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.12.020 · Cited by 885
A survey on unsupervised outlier detection in high‐dimensional numerical data
Zimek, A., Schubert, E., & Kriegel, H. (2012) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11161 · Cited by 864
Tsai, C.-W., Lai, C.-F., Chao, H.-C., & Vasilakos, A. V. (2015) · Journal of Big Data · doi:10.1186/s40537-015-0030-3 · Cited by 818
Cybersecurity data science: an overview from machine learning perspective
Sarker, I. H., Kayes, A. S. M., Badsha, S., Alqahtani, H., Watters, P., & Ng, A. (2020) · Journal of Big Data · doi:10.1186/s40537-020-00318-5 · Cited by 766
A reference model for learning analytics
Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012) · International Journal of Technology Enhanced Learning · doi:10.1504/ijtel.2012.051815 · Cited by 702
Xie, J., Yu, F. R., Huang, T., Xie, R., Liu, J., Wang, C., & Liu, Y. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2866942 · Cited by 700
Big Data Research in Information Systems: Toward an Inclusive Research Agenda
University of Virginia, Abbasi, A., Sarker, S., University of Virginia, Aalto University, Chiang, R., & University of Cincinnati (2016) · Journal of the Association for Information Systems · doi:10.17705/1jais.00423 · Cited by 688
Ensemble of keyword extraction methods and classifiers in text classification
Onan, A., Korukoğlu, S., & Bulut, H. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.03.045 · Cited by 630
Data Mining for Internet of Things: A Survey
Tsai, C.-W., Lai, C.-F., Chiang, M.-C., & Yang, L. T. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2013.103013.00206 · Cited by 620
Uncertainty in Big Data Analytics: Survey, Opportunities, and Challenges
Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019) · Journal Of Big Data · doi:10.1186/s40537-019-0206-3 · Cited by 620
Cognitive internet of things: A new paradigm beyond connection
Wu, Q., Ding, G., Xu, Y., Feng, S., Du, Z., Wang, J., & Long, K. (2014) · IEEE Internet of Things Journal · doi:10.1109/jiot.2014.2311513 · Cited by 550
Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019) · Telematics and Informatics · doi:10.1016/j.tele.2019.01.007 · Cited by 564
Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00765-8 · Cited by 570
Big Data: Survey, Technologies, Opportunities, and Challenges
Khan, N., Yaqoob, I., Hashem, I. A. T., Inayat, Z., Mahmoud Ali, W. K., Alam, M., Shiraz, M., & Gani, A. (2014) · The Scientific World Journal · doi:10.1155/2014/712826 · Cited by 547
Meta-analysis of cellular toxicity for cadmium-containing quantum dots
Oh, E., Liu, R., Nel, A., Gemill, K. B., Bilal, M., Cohen, Y., & Medintz, I. L. (2016) · Nature Nanotechnology · doi:10.1038/nnano.2015.338 · Cited by 502
Developing a Hybrid Intrusion Detection System Using Data Mining for Power Systems
Pan, S., Morris, T., & Adhikari, U. (2015) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2015.2409775 · Cited by 457
Data fusion in intelligent transportation systems: Progress and challenges – A survey
Faouzi, N.-E. E., Leung, H., & Kurian, A. (2011) · Information Fusion · doi:10.1016/j.inffus.2010.06.001 · Cited by 444
Addressing the class imbalance problem in medical datasets
Rahman, M. M., & Davis, D. N. (2013) · International Journal of Machine Learning and Computing · doi:10.7763/ijmlc.2013.v3.307 · Cited by 387
A survey of Knowledge Discovery and Data Mining process models
KURGAN, L. A., & MUSILEK, P. (2006) · The Knowledge Engineering Review · doi:10.1017/s0269888906000737 · Cited by 391
Mental health monitoring with multimodal sensing and machine learning: A survey
Garcia-Ceja, E., Riegler, M., Nordgreen, T., Jakobsen, P., Oedegaard, K. J., & Tørresen, J. (2018) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2018.09.003 · Cited by 356
Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews
Kang, H., Yoo, S. J., & Han, D. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.11.107 · Cited by 336
IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model
Sarker, I. H., Abushark, Y. B., Alsolami, F., & Khan, A. I. (2020) · Symmetry · doi:10.3390/sym12050754 · Cited by 356
Tutorial on practical tips of the most influential data preprocessing algorithms in data mining
García, S., Luengo, J., & Herrera, F. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.12.006 · Cited by 310
Sarker, I. H., Kayes, A. S. M., & Watters, P. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0219-y · Cited by 287
Analysis of the ISIC image datasets: Usage, benchmarks and recommendations
Cassidy, B., Kendrick, C., Brodzicki, A., Jaworek-Korjakowska, J., & Yap, M. H. (2022) · Medical Image Analysis · doi:10.1016/j.media.2021.102305 · Cited by 307
A taxonomy of privacy-preserving record linkage techniques
Vatsalan, D., Christen, P., & Verykios, V. S. (2013) · Information Systems · doi:10.1016/j.is.2012.11.005 · Cited by 295
Decision tree and PCA-based fault diagnosis of rotating machinery
Sun, W., Chen, J., & Li, J. (2007) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2006.06.010 · Cited by 272
A Machine Learning Security Framework for Iot Systems
Bagaa, M., Taleb, T., Bernabe, J. B., & Skarmeta, A. (2020) · IEEE Access · doi:10.1109/access.2020.2996214 · Cited by 271
LogSig: generating system events from raw textual logs
Tang, L., Li, T., & Perng, C.-S. (2011) · Proceedings of the 20th ACM international conference on Information and knowledge management · doi:10.1145/2063576.2063690 · Cited by 244
Pospieszny, P., Czarnacka-Chrobot, B., & Kobylinski, A. (2018) · Journal of Systems and Software · doi:10.1016/j.jss.2017.11.066 · Cited by 247
A survey of Clustering Algorithms
Rokach, L. (2009) · Data Mining and Knowledge Discovery Handbook · doi:10.1007/978-0-387-09823-4_14 · Cited by 245
Empirical Study on Applications of Data Mining Techniques in Healthcare
Kaur, H., & Wasan, S. K. (2006) · Journal of Computer Science · doi:10.3844/jcssp.2006.194.200 · Cited by 233
Ringel-Kulka, T., Cheng, J., Ringel, Y., Salojärvi, J., Carroll, I., Palva, A., de Vos, W. M., & Satokari, R. (2013) · PLoS ONE · doi:10.1371/journal.pone.0064315 · Cited by 230
Real-time scheduling for a smart factory using a reinforcement learning approach
Shiue, Y.-R., Lee, K.-C., & Su, C.-T. (2018) · Computers & Industrial Engineering · doi:10.1016/j.cie.2018.03.039 · Cited by 225
A Survey of Data-driven and Knowledge-aware eXplainable AI
Li, X.-H., Cao, C. C., Shi, Y., Bai, W., Gao, H., Qiu, L., Wang, C., Gao, Y., Zhang, S., Xue, X., et al. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2983930 · Cited by 224
Giri, D., Rajendra Acharya, U., Martis, R. J., Vinitha Sree, S., Lim, T.-C., Ahamed, T., & Suri, J. S. (2013) · Knowledge-Based Systems · doi:10.1016/j.knosys.2012.08.011 · Cited by 221
Discrete Wavelet transform-based time series analysis and mining
Chaovalit, P., Gangopadhyay, A., Karabatis, G., & Chen, Z. (2011) · ACM Computing Surveys · doi:10.1145/1883612.1883613 · Cited by 218
Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions
Sarker, I. H., Hoque, M. M., Uddin, M. K., & Alsanoosy, T. (2021) · Mobile Networks and Applications · doi:10.1007/s11036-020-01650-z · Cited by 207
What to teach? Strategies for developing digital competency in preservice teacher training
Howard, S. K., Tondeur, J., Ma, J., & Yang, J. (2021) · Computers & Education · doi:10.1016/j.compedu.2021.104149 · Cited by 215
Drought forecasting by ANN, ANFIS, and SVM and comparison of the models
Mokhtarzad, M., Eskandari, F., Jamshidi Vanjani, N., & Arabasadi, A. (2017) · Environmental Earth Sciences · doi:10.1007/s12665-017-7064-0 · Cited by 203
He is a Stud, She is a Slut! A Meta-Analysis on the Continued Existence of Sexual Double Standards
Endendijk, J. J., van Baar, A. L., & Deković, M. (2020) · Personality and Social Psychology Review · doi:10.1177/1088868319891310 · Cited by 200
An analytical method for diseases prediction using machine learning techniques
Nilashi, M., Ibrahim, O. B., Ahmadi, H., & Shahmoradi, L. (2017) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2017.06.011 · Cited by 190
Cyber threat detection based on artificial neural networks using event profiles
Lee, J., Kim, J., Kim, I., & Han, K. (2019) · IEEE Access · doi:10.1109/access.2019.2953095 · Cited by 188
“TF-IDF is a statistical technique to index the term according to their importance, as it is based on vectors that represent the term frequency as well as term presence [43].”
MethodVivekanandan, T., & Sriman Narayana Iyengar, N. C. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.09.011 · Cited by 185
Predicting Instructor Performance Using Data Mining Techniques in Higher Education
Agaoglu, M. (2016) · IEEE Access · doi:10.1109/access.2016.2568756 · Cited by 183
Pan, S., Morris, T., & Adhikari, U. (2015) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2015.2420951 · Cited by 180
(2003) · Cited by 165
A comprehensive survey of numeric and symbolic outlier mining techniques
Agyemang, M., Barker, K., & Alhajj, R. (2006) · Intelligent Data Analysis · doi:10.3233/ida-2006-10604 · Cited by 164
Xu, Y., Zhang, R., Zhao, J., Dong, Z. Y., Wang, D., Yang, H., & Wong, K. P. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2441706 · Cited by 157
Exploring visitor movement patterns in natural recreational areas
Orellana, D., Bregt, A. K., Ligtenberg, A., & Wachowicz, M. (2012) · Tourism Management · doi:10.1016/j.tourman.2011.07.010 · Cited by 156
Duodenal microbiota composition and mucosal homeostasis in pediatric celiac disease
Cheng, J., Kalliomäki, M., Heilig, H. G., Palva, A., Lähteenoja, H., de Vos, W. M., Salojärvi, J., & Satokari, R. (2013) · BMC Gastroenterology · doi:10.1186/1471-230x-13-113 · Cited by 152
Mahato, S., & Paul, S. (2020) · Journal of Medical Systems · doi:10.1007/s10916-019-1486-z · Cited by 148
Practical Root Cause Localization for Microservice Systems via Trace Analysis
Li, Z., Chen, J., Jiao, R., Zhao, N., Wang, Z., Zhang, S., Wu, Y., Jiang, L., Yan, L., Wang, Z., et al. (2021) · 2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS) · doi:10.1109/iwqos52092.2021.9521340 · Cited by 159
The Role of Hubness in Clustering High-Dimensional Data
Tomasev, N., Radovanovic, M., Mladenic, D., & Ivanovic, M. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.25 · Cited by 142
Al-Hassan, M., Lu, H., & Lu, J. (2015) · Decision Support Systems · doi:10.1016/j.dss.2015.02.001 · Cited by 136
Cluster Analysis of Smart Metering Data: An Implementation in Practice
Flath, C., Nicolay, D., Conte, T., van Dinther, C., & Filipova-Neumann, L. (2012) · Business & Information Systems Engineering · doi:10.1007/s12599-011-0201-5 · Cited by 137
Event Prediction in the Big Data Era: A Systematic Survey
Zhao, L. (2022) · ACM Computing Surveys · doi:10.1145/3450287 · Cited by 135
Integrated multi-omics analysis of ovarian cancer using variational autoencoders
Hira, M. T., Razzaque, M. A., Angione, C., Scrivens, J., Sawan, S., & Sarker, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-85285-4 · Cited by 134
Visual pattern mining in histology image collections using bag of features
Cruz-Roa, A., Caicedo, J. C., & González, F. A. (2011) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2011.04.010 · Cited by 133
Sarker, I. H. (2021) · Internet of Things · doi:10.1016/j.iot.2021.100393 · Cited by 129
Systematic review of research on artificial intelligence in K–12 education (2017–2022)
Martin, F., Zhuang, M., & Schaefer, D. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100195 · Cited by 132
Context-Aware Activity Recognition and Anomaly Detection in Video
Zhu, Y., Nayak, N. M., & Roy-Chowdhury, A. K. (2013) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2012.2234722 · Cited by 121
Systematic analysis of genome-wide fitness data in yeast reveals novel gene function and drug action
Hillenmeyer, M. E., Ericson, E., Davis, R. W., Nislow, C., Koller, D., & Giaever, G. (2010) · Genome Biology · doi:10.1186/gb-2010-11-3-r30 · Cited by 112
A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life Cycle
Shahriar, S., Allana, S., Hazratifard, S. M., & Dara, R. (2023) · IEEE Access · doi:10.1109/access.2023.3287195 · Cited by 114
A machine learning based robust prediction model for real-life mobile phone data
Sarker, I. H. (2019) · Internet of Things · doi:10.1016/j.iot.2019.01.007 · Cited by 113
Machine Learning for Human Learners: Opportunities, Issues, Tensions and Threats
Webb, M. E., Fluck, A., Magenheim, J., Malyn-Smith, J., Waters, J., Deschênes, M., & Zagami, J. (2021) · Educational Technology Research and Development · doi:10.1007/s11423-020-09858-2 · Cited by 116
Using data mining techniques to predict hospitalization of hemodialysis patients
Yeh, J.-Y., Wu, T.-H., & Tsao, C.-W. (2011) · Decision Support Systems · doi:10.1016/j.dss.2010.11.001 · Cited by 110
Evaluating the effect of dataset size on predictive model using supervised learning technique
Ajiboye, A. R., Abdullah-Arshah, R., Qin, H., & Isah-Kebbe, H. (2015) · International Journal of Computer Systems & Software Engineering · doi:10.15282/ijsecs.1.2015.6.0006 · Cited by 104
Nancy Jane, Y., Khanna Nehemiah, H., & Arputharaj, K. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.01.014 · Cited by 98
Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data
Sieverink, F., Kelders, S., Poel, M., & van Gemert-Pijnen, L. (2017) · JMIR Research Protocols · doi:10.2196/resprot.6452 · Cited by 88
Real-time analysis of physiological data to support medical applications
Apiletti, D., Baralis, E., Bruno, G., & Cerquitelli, T. (2009) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2008.2010702 · Cited by 87
A proposed social network analysis platform for big data analytics
Chang, V. (2018) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2017.11.002 · Cited by 86
Sarker, I. H., & Kayes, A. S. M. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102762 · Cited by 75
Valsamis, A., Tserpes, K., Zissis, D., Anagnostopoulos, D., & Varvarigou, T. (2017) · Journal of Systems and Software · doi:10.1016/j.jss.2016.06.016 · Cited by 82
Lionetti, E., Castellaneta, S., Pulvirenti, A., Tonutti, E., Francavilla, R., Fasano, A., & Catassi, C. (2012) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2012.05.008 · Cited by 82
Individualized Time-Series Segmentation for Mining Mobile Phone User Behavior
Sarker, I. H., Colman, A., Kabir, M. A., & Han, J. (2018) · The Computer Journal · doi:10.1093/comjnl/bxx082 · Cited by 80
Self-citationZhang, C., Zhao, Y., Fan, C., Li, T., Zhang, X., & Li, J. (2020) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2020.115261 · Cited by 79
Big data analytics in healthcare
Guo, C., & Chen, J. (2023) · Translational Systems Sciences · doi:10.1007/978-981-99-1075-5_2 · Cited by 90
Singh, K. N., Devi, S. D., Devi, H. M., & Mahanta, A. K. (2022) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2022.100061 · Cited by 75
(2006) · Cited by 72
Mining outlying aspects on numeric data
Duan, L., Tang, G., Pei, J., Bailey, J., Campbell, A., & Tang, C. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0398-2 · Cited by 70
(2012) · Cited by 70
Software plagiarism detection: a graph-based approach
Chae, D.-K., Ha, J., Kim, S.-W., Kang, B., & Im, E. G. (2013) · Proceedings of the 22nd ACM international conference on Information & Knowledge Management · doi:10.1145/2505515.2507848 · Cited by 66
A systematic review of provenance systems
Pérez, B., Rubio, J., & Sáenz-Adán, C. (2018) · Knowledge and Information Systems · doi:10.1007/s10115-018-1164-3 · Cited by 66
RecencyMiner: mining recency-based personalized behavior from contextual smartphone data
Sarker, I. H., Colman, A., & Han, J. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0211-6 · Cited by 65
Self-citationTrust Modeling for Networked Organizations Using Reputation and Collaboration Estimates
Lavra, N., Ljubi, P., Urbani, T., Papa, G., Jermol, M., & Bollhalter, S. (2007) · IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2006.889531 · Cited by 65
On Multi-Relational Link Prediction with Bilinear Models
Wang, Y., Gemulla, R., & Li, H. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11738 · Cited by 65
Esparza, G. G., de-Luna, A., Zezzatti, A. O., Hernandez, A., Ponce, J., Álvarez, M., Cossio, E., & de Jesus Nava, J. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-62410-5_19 · Cited by 65
Metadata
- Venue
- Choice Reviews Online
- Publisher
- American Library Association
- Published
- 2012-02-01
- Volume
- 49
- Issue
- 06
- Pages
- 49-3305-49-3305
- ISSN
- 0009-4978 (ISSN-L)
- 1523-8253
- OpenAlex
- W2140190241
- Cited by
- 28,531
- References
- 22
- Type
- book review
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
