Laurence A. F. Park

Research

Mon, 01 Jan 0001 00:00:00 +0000

Research Interests

Information Retrieval - including topics such as Web link analysis, probabilistic latent semantic analysis, latent Dirichlet allocation, evaluation, community tagging, query expansion
Data Mining - kernel PCA, non-negative matrix factorisation, spectral graph analysis, co-clustering, VAT, coVAT
Machine Learning - support vector machines, conditional random fields, neural networks, self-organising maps
Data Compression - managing large sparse or dense data sets for efficient information retrieval
Signal Processing - wavelet transforms, Fourier transforms

Research Students (PhD)

Yong Zhen Guo - Predicting Web user behaviour (Graduated 2010)
Uyen Nguyen - Machine learning for Information retrieval (Graduted 2013)
Sunil Randeni Kadupitige - Optimising Document Models (Graduated 2022)
Sayed Qayyumi - Machine Learning on Small Training Sets (Graduated 2026)
Tanzila Chowdhury - Modelling Drug Resistance in Tuberculosis (Graduated 2026)
Kieran Luken - Modelling Redshift in Astronomical Objects (Graduated 2024)
Miranda Yew - Classification and alignment of DRAGNs (Graduated 2023)
Vishal Patel - Trajectory Prediction of Vehicles in Heterogenious Environments (Submitted 2026)
Yubraj Jung Shah - Efficient Object Tracking using Deep Learning (Submitted 2026)
Kate Jones - Multilingualism and its effect on language formation (Submitted 2026)
Stuart Fitzpatrick - Identifying style and content in text
Elizabeth Camilleri - Multi-agent Reinforcement Learning
Piyushan Gunawardhana - Identifying essential athlete features for international cricket

Selected Publications

Publications for 2025

[1]	Laurence AF Park and Jesse Read. Estimating multi-label expected accuracy using labelset distributions. IEEE Transactions on Knowledge and Data Engineering, 2025. [ bib \| DOI ]
[2]	Laurence AF Park and Jesse Read. Determining the need for multi-label classifiers by measuring unexplained covariance. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 250--261. Springer, 2025. [ bib \| DOI ]
[3]	Ray P Norris, Miranda Yew, Evan J Crawford, Nikhel Gupta, Lawrence Rudnick, Heinz Andernach, Miroslav D Filipović, Yjan Gordon, Andrew Hopkins, Laurence Park, et al. Emu and the dragns i: A catalogue of dragns. Publications of the Astronomical Society of Australia, 42:e124, 2025. [ bib \| DOI ]
[4]	Laurence AF Park and Jesse Read. Estimating conditional covariance between labels for multilabel data. arXiv preprint arXiv:2508.18951, 2025. [ bib \| DOI ]
[5]	Gizem Intepe, Yingtong Liang, and Laurence Park. Evaluating structural preprocessing in rag for academic curriculum applications. In Proceedings of the 23rd Australasian Data Science and Machine Learning Conference (AusDM'25) (to appear), 2025. [ bib ]

Publications for 2024

[1]	Tanzila K Chowdhury, Laurence AF Park, Glenn Stone, Mark M Tanaka, and Andrew Francis. Estimating the relative contribution of transmission to the prevalence of drug resistance in tuberculosis. In Data Driven Science for Clinically Actionable Knowledge in Diseases, pages 64--91. Chapman and Hall/CRC, 2024. [ bib \| DOI \| .pdf ]
[2]	Sumal Randeni, Kenan M Matawie, and Laurence AF Park. Residual analysis in information retrieval models. In 38th International Workshop on Statistical Modelling, page 265, 2024. [ bib ]
[3]	Yubraj Jung Shah, Yi Guo, Laurence AF Park, and Oliver Obst. Selective frame analysis for efficient object tracking: Balancing speed with accuracy in MOT systems. In International Conference on Multi-disciplinary Trends in Artificial Intelligence, pages 245--256. Springer, 2024. [ bib \| DOI ]

Publications for 2023

[1]	James East, Michael Hendriksen, and Laurence Park. On the enumeration of integer tetrahedra. Computational Geometry, 108:101915, 2023. [ bib \| DOI \| .pdf ]
[2]	Kieran J Luken, Ray P Norris, X Rosalind Wang, Laurence AF Park, Ying Guo, and MD Filipović. Measuring photometric redshifts for high-redshift radio source surveys. Publications of the Astronomical Society of Australia, 40:e039, 2023. [ bib \| DOI \| .pdf ]
[3]	Tomas Trescak, Laurence A Park, and Mesut Kocyigit. Jobiq: recommending study pathways based on career choices. In Proceedings of the 15th International Conference on Computer Supported Education (CSEDU 2023): Volume 1, 21-23 April 2023, Prague, Czech Republic, pages 137--145, 2023. [ bib \| DOI \| .pdf ]
[4]	Sayed Waleed Qayyumi, Laurence AF Park, and Oliver Obst. Few-shot and transfer learning with manifold distributed datasets. In Australasian Conference on Data Science and Machine Learning, pages 137--149. Springer, 2023. [ bib ]

Publications for 2022

[1]	Andrew Barnett, Chunjie Cheng, Mahmut Horasan, Yaping He, and Laurence Park. Fire load density distribution in school buildings and statistical modelling. Fire Technology, 58(1):503--521, 2022. [ bib \| DOI \| .pdf ]
[2]	Kieran J Luken, Ray P Norris, Laurence AF Park, X Rosalind Wang, and MD Filipović. Estimating galaxy redshift in radio-selected datasets using machine learning. Astronomy and Computing, 39:100557, 2022. [ bib \| DOI \| .pdf ]
[3]	Laurence AF Park, Mohadeseh Ganji, Emir Demirovic, Jeffrey Chan, Peter Stuckey, James Bailey, Christopher Leckie, and Rao Kotagiri. Modelling zeros in blockmodelling. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 187--198. Springer, 2022. [ bib \| DOI \| .pdf ]
[4]	Laurence AF Park and Jesse Read. Estimating multi-label accuracy using labelset distributions. arXiv preprint arXiv:2209.04163, 2022. [ bib \| DOI \| .pdf ]
[5]	Sayed Waleed Qayyumi, Laurence AF Park, and Oliver Obst. Active learning for knn using instance impact. In Australasian Joint Conference on Artificial Intelligence, pages 413--426. Springer, 2022. [ bib \| DOI \| .pdf ]
[6]	Stuart Fitzpatrick, Laurence Park, and Oliver Obst. Measuring content preservation in textual style transfer. In Australasian Conference on Data Mining, pages 3--14. Springer, 2022. [ bib \| DOI \| .pdf ]
[7]	Laurence AF Park, Heitor Murilo Gomes, Maryam Doborjeh, Yee Ling Boo, Yun Sing Koh, Yanchang Zhao, Graham Williams, and Simeon Simoff, editors. Data Mining: 20th Australasian Conference, AusDM 2022, Western Sydney, Australia, December 12--15, 2022, Proceedings. Springer Nature, 2022. [ bib \| DOI \| http ]

Publications for 2021

[1]

Rehez Ahlip, Laurence AF Park, Ante Prodan, and Stephen Weissenhofer. Forward start options under heston affine jump-diffusions and stochastic interest rate. International Journal of Financial Engineering, 8(01):2150005, 2021. [ bib | DOI ]

[2]

Ray P. Norris, Huib T. Intema, Anna D. Kapinska, Baerbel S. Koribalski, Emil Lenc, L. Rudnick, Rami Alsaberi, Craig Anderson, G. E. Anderson, E. Crawford, Roland Crocker, Jayanne English, Miroslav D. Filipovic, Tim Galvin, Andrew M. Hopkins, Natasha Hurley-Walker, Susumu Inoue, Kieran Luken, Peter Macgregor, Pero Manojlovic, Josh Marvil, Andrew N. O'Brien, Laurence A. F. Park, Wasim Raja, Devika Shobhana, Tiziana Venturi, Jordan D. Collier, Catherine Hale, Aidan Hotan, Vanessa Moss, and Matthew Whiting. Unexpected circular radio objects at high galactic latitude. Publications of the Astronomical Society of Australia, 2021. [ bib | DOI | arXiv | .pdf ]

Publications for 2020

[1]	Laurence A. F. Park, Yi Guo, and Jesse Read. Assessing the multi-labelness of multi-label data. In Machine Learning and Knowledge Discovery in Databases, ECML PKDD, Lecture Notes in Artificial Intelligence. Springer International Publishing, 2020. [ bib \| DOI \| .pdf ]
[2]	Andrew Francis, Yi Guo, Paul Hurley, Oliver Obst, Laurence AF Park, Mark Tanaka, Russell Thomson, and Rosalind Wang. Projected icu and mortuary load due to covid-19 in sydney. medRxiv, 2020. [ bib \| DOI \| .pdf ]
[3]	Alistair Shilton, Sunil Gupta, Santu Rana, Pratibha Vellanki, Cheng Li, Svetha Venkatesh, Laurence Park, Alessandra Sutti, David Rubin, Thomas Dorin, Alireza Vahid, Murray Height, and Teo Slezak. Accelerated bayesian optimisation through weight-prior tuning. volume 108 of Proceedings of Machine Learning Research, pages 635--645, Online, 26--28 Aug 2020. PMLR. [ bib \| .pdf \| .pdf \| Abstract ]
[4]	Miranda Yew, Miroslav D Filipović, Milorad Stupar, Sean D Points, Manami Sasaki, Pierre Maggi, Frank Haberl, Patrick J Kavanagh, Quentin A Parker, Evan J Crawford, Branislav Vukotić, Dejan Urošević, Hidetoshi Sano, Ivo R Seitenzahl, Gavin Rowell, Denis Leahy, Luke M Bozzetto, Chandreyee Maitra, Howard Leverenz, Jeffrey L Payne, Laurence A F Park, Rami Z E Alsaberi, and Thomas G Pannuti. New optically identified supernova remnants in the Large Magellanic Cloud. Monthly Notices of the Royal Astronomical Society, 500(2):2336--2358, 11 2020. [ bib \| DOI \| .pdf \| Abstract ]

Publications for 2019

[1]	Mohadeseh Ganji, Jeffrey Chan, Peter. J. Stuckey, James Bailey, Christopher Leckie, Kotagiri Ramamohanarao, and Laurence Park. Semi-supervised blockmodelling with pairwise guidance. In Michele Berlingerio, Francesco Bonchi, Thomas Gärtner, Neil Hurley, and Georgiana Ifrim, editors, Machine Learning and Knowledge Discovery in Databases, ECML PKDD, Lecture Notes in Artificial Intelligence, pages 158--174. Springer International Publishing, Dublin, Ireland, 2019. [ bib \| DOI \| .pdf ]
[2]	Laurence A. F. Park and Jesse Read. A blended metric for multi-label optimisation and evaluation. In Michele Berlingerio, Francesco Bonchi, Thomas Gärtner, Neil Hurley, and Georgiana Ifrim, editors, Machine Learning and Knowledge Discovery in Databases, ECML PKDD, Lecture Notes in Artificial Intelligence, pages 719--734. Springer International Publishing, Cham, 2019. [ bib \| DOI \| .pdf ]
[3]	Luke Munn, Tsvetelina Hristova, Liam Magee, Debra Bourdignon, Tim Horan, Tamir Levin, Tal Nathan, and Laurence Park. The new privacy: Emerging standards for cloud-based security. 2019. [ bib \| DOI ]
[4]	Ric Lovell, Dawn Scott, and Laurence Park. Soccer velocity thresholds: do we really know what’s best? Science and Medicine in Football, 3(1):85--86, 2019. [ bib \| .pdf ]
[5]	Kieran J Luken, Ray P Norris, and Laurence AF Park. Preliminary results of using k-nearest-neighbor regression to estimate the redshift of radio-selected data sets. Publications of the Astronomical Society of the Pacific, 131(1004):108003, 2019. [ bib \| DOI \| .pdf ]
[6]	Nicholas O Ralph, Ray P Norris, Gu Fang, Laurence AF Park, Timothy J Galvin, Matthew J Alger, Heinz Andernach, Chris Lintott, Lawrence Rudnick, Stanislav Shabala, et al. Radio galaxy zoo: Unsupervised clustering of convolutionally auto-encoded radio-astronomical images. Publications of the Astronomical Society of the Pacific, 131(1004):108011, 2019. [ bib \| DOI \| .pdf ]

Publications for 2018

[1]	Rehez Ahlip, AF Laurence, and Ante Prodan. Semi-analytical option pricing under double heston jump-diffusion hybrid model. Journal of Mathematical Sciences and Modelling, 1(3):138--152, 2018. [ bib \| DOI \| .pdf ]
[2]	Quentin Roper, Miroslav Filipovic, Glenn E Allen, Hidetoshi Sano, Laurence Park, Thomas G Pannuti, Manami Sasaki, Frank Haberl, Patrick J Kavanagh, Yumiko Yamane, Satoshi Yoshii, Kosuke Fujii, Yasuo Fukui, and Ivo R Seitenzahl. An x-ray expansion and proper motion study of the magellanic cloud supernova remnant j0509–6731 with the chandra x-ray observatory. Monthly Notices of the Royal Astronomical Society, 479:1800–--1806, 2018. [ bib \| DOI \| .pdf ]
[3]	Laurence A. F. Park, Dawn Scott, and Ric Lovell. Velocity zone classification in elite women’s football: where do we draw the lines? Science and Medicine in Football, pages 1--8, 2018. [ bib \| DOI \| .pdf ]
[4]	Yi Guo, Simon Green, Laurence A. F. Park, and Lauren Rispen. Left ventricle volume measuring using echocardiography sequences. In 2018 Digital Image Computing: Techniques and Applications (DICTA), pages 1--8, Dec 2018. [ bib \| DOI \| .pdf \| Abstract ]

Publications for 2017

[1]	Sunil Randeni, Kenan M Matawie, and Laurence A. F. Park. An investigation into the use of document scores for optimisation over rank-biased precision. In Asian Information Retrieval Symposium, pages 197--209. Springer, 2017. [ bib \| DOI ]
[2]	Rehez Ahlip, Laurence A. F. Park, and Ante Prodan. Pricing currency options in the heston/cir double exponential jump-diffusion model. International Journal of Financial Engineering, 4(01), 2017. [ bib \| DOI \| .pdf ]
[3]	Yaping He and Laurence A. F. Park. A statistical analysis of occurrence and association between structural fire hazards in heritage housing. Fire Safety Journal, 90:169--180, 2017. [ bib \| DOI \| .pdf ]

Publications for 2016

[1]	Laurence A. F. Park. Uncertainty in rank-biased precision. In Proceedings of the 21st Australasian Document Computing Symposium, ADCS '16, New York, NY, USA, 2016. ACM. [ bib \| DOI \| .pdf ]
[2]	Laurence A. F. Park, James C. Bezdek, Christopher Leckie, Kotagiri Ramamohanarao, James Bailey, and Marimuthu Palaniswami. Visual assessment of clustering tendency for incomplete data. IEEE Transactions on Knowledge and Data Engineering, 2016. [ bib \| DOI \| .pdf ]
[3]	Laurence A. F. Park and Glenn Stone. The effect on accuracy of tweet sample size for hashtag segmentation dictionary construction. In Proceedings of the 2016 Pacific Asia Knowledge Discovery and Data Mining Conference (PAKDD), PAKDD, 2016. [ bib \| DOI \| .pdf ]

Publications for 2015

[1]	Laurence A. F. Park and Glenn Stone. The effect of assessor coverage and assessor accuracy on rank aggregation precision. In Proceedings of the 20th Australasian Document Computing Symposium, ADCS '15, pages 6:1--6:4, New York, NY, USA, 2015. ACM. [ bib \| DOI \| .pdf ]
[2]	Laurence A. F. Park and Simeon Simoff. Using entropy as a measure of acceptance for multi-label classification. In Elisa Fromont, Tijl De Bie, and Matthijs van Leeuwen, editors, Advances in Intelligent Data Analysis XIV, pages 217--228. Springer, 2015. [ bib \| DOI \| .pdf ]
[3]	Laurence A. F. Park and Sarvnaz Karimi, editors. ADCS '15: Proceedings of the 20th Australasian Document Computing Symposium. ACM, New York, NY, USA, 2015. [ bib \| DOI \| http ]

Publications for 2014

[1]	J. Shane Culpepper, Laurence Park, and Guido Zuccon, editors. ADCS '14: Proceedings of the 2014 Australasian Document Computing Symposium. ACM, New York, NY, USA, 2014. [ bib \| DOI \| http ]
[2]	Laurence A. F. Park and Glenn Stone. By the power of grayskull: Small sample statistical power in information retrieval evaluation. In Proceedings of the 2014 Australasian Document Computing Symposium, ADCS '14, pages 101:101--101:104, New York, NY, USA, 2014. ACM. [ bib \| DOI \| .pdf ]
[3]	Laurence A. F. Park and Simeon Simoff. Second order probabilistic models for within-document novelty detection in academic articles. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR '14, pages 1103--1106, New York, NY, USA, 2014. ACM. [ bib \| DOI \| .pdf ]
[4]	Laurence A. F. Park and Glenn Stone. Inducing controlled error over variable length ranked lists. In Vincent S. Tseng, Tu Bao Ho, Zhi-Hua Zhou, ArbeeL.P. Chen, and Hung-Yu Kao, editors, Advances in Knowledge Discovery and Data Mining, volume 8444 of Lecture Notes in Computer Science, pages 259--270. Springer International Publishing, 2014. [ bib \| DOI \| .pdf ]

Publications for 2013

[1]	Uyen T. V. Nguyen, Alauddin Bhuiyan, Laurence A. F. Park, and Kotagiri Ramamohanarao. An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recognition, 46(3):703--715, March 2013. [ bib \| DOI \| .pdf ]
[2]	Laurence A. F. Park and Simeon Simoff. Power walk: Revisiting the random surfer. In Proceedings of the 18th Australasian Document Computing Symposium, ADCS '13, pages 50--57, New York, NY, USA, 2013. ACM. [ bib \| DOI \| .pdf ]
[3]	Uyen T. V. Nguyen, Alauddin Bhuiyan, Laurence A. F. Park, Ryo Kawasaki, Tien Y Wong, Jie J Wang, Paul Mitchell, and Kotagiri Ramamohanarao. Automated quantification of retinal arteriovenous nicking from colour fundus images. In Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, pages 5865--5868. IEEE, 2013. [ bib \| DOI ]
[4]	Uyen T. V. Nguyen, Alauddin Bhuiyan, Laurence A. F. Park, Ryo Kawasaki, Tien Y Wong, Jie Jin Wang, Paul Mitchell, and Kotagiri Ramamohanarao. An automated method for retinal arteriovenous nicking quantification from color fundus images. Biomedical Engineering, IEEE Transactions on, 60(11):3194--3203, 2013. [ bib \| DOI ]

Publications for 2012

[1]	U. T. V. Nguyen, A Bhuiyan, L. A. F. Park, R Kawasaki, T. Y. Wong, and K Ramamohanarao. Automatic detection of retinal vascular landmark features for colour fundus image matching and patient longitudinal study. In 2013 IEEE International Conference on Image Processing (ICIP'13), 2012. [ bib \| DOI \| .pdf ]
[2]	Uyen Nguyen, Laurence A. F. Park, Alauddin Bhuiyan, and Ramamohanarao Kotagiri. A quantitative measure for retinal blood vessel segmentation evaluation. International Journal of Computer Vision and Signal Processing, 1(1):1--8, 2012. [ bib \| .pdf ]
[3]	Mazin Aouf and Laurence A. F. Park. Approximate document outlier detection using random spectral projection. In Michael Thielscher and Dongmo Zhang, editors, AI 2012: Advances in Artificial Intelligence: 25th Australasian Joint Conference, Sydney, Australia, December 4-7, 2012. Proceedings, pages 579--590. Springer Berlin Heidelberg, Berlin, Heidelberg, 2012. [ bib \| DOI \| .pdf ]

Publications for 2011

[1]	Laurence A. F. Park and Kotagiri Ramamohanarao. Multi-resolution web link analysis using generalised link relations. IEEE Transactions of Knowledge and Data Engineering, 23(11):1691 -- 1703, November 2011. [ bib \| DOI \| .pdf ]
[2]	Uyen T. V. Nguyen, Alauddin Bhuiyan, Kotagiri Ramamohanarao, and Laurence A. F. Park. An effective supervised framework for retinal blood vessel segmentation using local standardisation and bagging. In Proceedings of the Second international conference on Machine learning in medical imaging, MLMI'11, pages 117--125, Berlin, Heidelberg, 2011. Springer-Verlag. [ bib \| http ]
[3]	Laurence A. F. Park. Examining document model residuals to provide feedback during information retrieval evaluation. In Paul Thomas, Sally Jo Cunningham, and Falk Scholer, editors, Proceedings of the Sixteenth Australasian Document Computing Symposium. ACM Press, 2011. [ bib \| .pdf ]
[4]	Laurence A. F. Park. Fast approximate text document clustering using compressive sampling. In Dimitrios Gunopulos, Thomas Hofmann, Donato Malerba, and Michalis Vazirgiannis, editors, Machine Learning and Knowledge Discovery in Databases, volume 6912 of Lecture Notes in Computer Science, pages 565--580. Springer Berlin / Heidelberg, 2011. [ bib \| DOI \| .pdf ]
[5]	Eric Beh, Laurence Park, and Kenneth Russell, editors. Proceedings of the 4th Applied Statistics Education and Research Collaboration (ASEARC) Conference. University of Wollongong (CSSM), 2011. [ bib \| .pdf ]
[6]	Masud Moshtaghi, Timothy C. Havens, James C. Bezdek, Laurence Park, Christopher Leckie, Sutharshan Rajasegarar, James M. Keller, and Marimuthu Palaniswami. Clustering ellipses for anomaly detection. Pattern Recognition, 44(1):55--69, 2011. [ bib \| DOI \| .pdf ]
[7]	Laurence A. F. Park. Bootstrap confidence intervals for mean average precision. In Proceedings of the Fourth ASEARC Conference, pages 51--54, 2011. [ bib \| .pdf ]

Publications for 2010

[1]	Laurence A. F. Park. Confidence intervals for information retrieval evaluation. In Andrew Turpin, Falk Scholer, and Andrew Trotman, editors, Proceedings of the Fifteenth Australasian Document Computing Symposium, 2010. [ bib \| .pdf ]
[2]	Yuye Zhang, Laurence A. F. Park, and Alistair Moffat. Click-based evidence for decaying weight distributions in search effectiveness metrics. The Journal of Information Retrieval, pages 1--24, 2010. [ bib \| DOI \| .pdf \| Abstract ]
[3]	James Bezdek, Timothy Havens, James Keller, Christopher Leckie, Laurence Park, Marimuthu Palaniswami, and Sutharshan Rajasegarar. Clustering elliptical anomalies in sensor networks. In Proceedings of 2010 Conference on Fuzzy Systems (FUZZ-IEEE), pages 909--916, Piscataway, NJ, 2010. IEEE Press. [ bib \| DOI \| .pdf ]

Publications for 2009

[1]	Laurence A. F. Park, Kotagiri Ramamohanarao, Christopher A. Leckie, and James C. Bezdek. Adapting spectral co-clustering to documents and words using latent semantic analysis. In AI 2009: Advances in Artificial Intelligence, volume 5866 of Lecture Notes in Computer Science, pages 301--311. Springer Berlin / Heidelberg, December 2009. [ bib \| DOI \| Abstract ]
[2]	Uyen T. V. Nguyen, Laurence A. F. Park, Liang Wang, and Kotagiri Ramamohanarao. A novel path-based clustering algorithm using multi-dimensional scaling. In AI 2009: Advances in Artificial Intelligence, volume 5866 of Lecture Notes in Computer Science, pages 280--290. Springer Berlin / Heidelberg, December 2009. [ bib \| DOI \| .pdf ]
[3]	Laurence A. F. Park and Kotagiri Ramamohanarao. Kernel latent semantic analysis using an information retrieval based kernel. In David Cheung, Il-Yeol Song, Wesley Chu, Xiaohua Hu, Jimmy Lin, Jiexun Li, and Zhiyong Peng, editors, Proceeding of the 18th ACM conference on Information and knowledge management., pages 1721--1724. The Association for Computing Machinery, November 2009. [ bib \| DOI \| .pdf ]
[4]	Yong Zhen Guo, Kotagiri Ramamohanarao, and Laurence A. F. Park. Web access latency reduction using CRF-based predictive caching. In Web Information Systems and Mining, volume 5854 of Lecture Notes in Computer Science, pages 31--44. Springer Berlin / Heidelberg, November 2009. [ bib \| DOI \| .pdf ]
[5]	Laurence A. F. Park and Kotagiri Ramamohanarao. The sensitivity of latent dirichlet allocation for information retrieval. In Wray Buntine, Marko Grobelnik, Dunja Mladenic, and John Shawe-Taylor, editors, Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD Proceedings, Part II, Lecture Notes in Artificial Intelligence, pages 176--188. Springer, Bled, Slovenia, September 2009. [ bib \| DOI \| .pdf ]
[6]	William Webber and Laurence A. F. Park. Score adjustment for correction of pooling bias. In Mark Sanderson, ChengXiang Zhai, Justin Zobel, James Allan, and Javed Aslam, editors, Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, pages 444--451, New York, USA, July 2009. ACM Press. [ bib \| DOI \| .pdf ]
[7]	Sri Ravana, Laurence A. F. Park, and Alistair Moffat. System scoring using partial prior information. In Mark Sanderson, ChengXiang Zhai, Justin Zobel, James Allan, and Javed Aslam, editors, Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, pages 788--789, New York, USA, July 2009. ACM Press. [ bib \| DOI \| .pdf ]
[8]	Justin Zobel, Alistair Moffat, and Laurence A. F. Park. Against recall: Is it persistence, cardinality, density, coverage, or totality? In ACM SIGIR Forum, volume 43, pages 3--15. ACM Press, June 2009. [ bib \| DOI \| .pdf ]
[9]	Yong Zhen Guo, Kotagiri Ramamohanarao, and Laurence A. F. Park. Grouped ECOC conditional random fields for prediction of web user behavior. In Advances in Knowledge Discovery and Data Mining, volume 5476 of Lecture Notes in Computer Science, pages 757--763. Springer Berlin / Heidelberg, April 2009. [ bib \| DOI \| .pdf ]
[10]	Laurence A. F. Park, James C. Bezdek, and Christopher A. Leckie. Visualisation of clusters in very large rectangular dissimilarity data. In G. Sen Gupta and S. C. Mukhopadhyay, editors, Proceedings of the Fourth International Conference on Autonomous Robots and Agents, pages 251--256, February 2009. [ bib \| DOI \| Abstract ]
[11]	Laurence A. F. Park and Kotagiri Ramamohanarao. An analysis of latent semantic term self-correlation. ACM Transactions on Information Systems, 27(2):1--35, 2009. [ bib \| DOI \| Abstract ]
[12]	Laurence A. F. Park and Kotagiri Ramamohanarao. Efficient storage and retrieval of probabilistic latent semantic information for information retrieval. The International Journal on Very Large Data Bases, 18(1):141--156, January 2009. [ bib \| DOI \| Abstract ]

Publications for 2008

[1]	Laurence A. F. Park and Kotagiri Ramamohanarao. Query expansion for the language modelling framework using the naive bayes assumption. In Takashi Washio, Einoshin Suzuki, Kai Ming Ting, and Akihiro Inokuchi, editors, The Twelfth Pacific-Asia Conference on Knowledge Discovery and Data Mining, number 5012 in LNCS, pages 681--688. Springer, May 2008. [ bib \| DOI \| Abstract ]
[2]	Laurence A. F. Park and Kotagiri Ramaohanarao. The effect of weighted term frequencies on probabilistic latent semantic term relationships. In Amihood Amir, Andrew Turpin, and Alistair Moffat, editors, The 5th String Processing and Information Retrieval Symposium, volume 5280/2009, pages 63--74, 2008. [ bib \| DOI \| .pdf \| Abstract ]
[3]	Yong Zhen Guo, Kotagiri Ramamohanarao, and Laurence A. F. Park. Web page prediction based on conditional random fields. In Proceedings of the 18th European Conference on Artificial Intelligence, pages 251--255, 2008. [ bib \| .pdf \| Abstract ]
[4]	Yong Zhen Guo, Kotagiri Ramamohanarao, and Laurence A. F. Park. Error correcting output coding-based conditional random fields for web page prediction. In Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence, 2008. [ bib \| DOI \| Abstract ]
[5]	Yuye Zhang, Laurence A. F. Park, and Alistair Moffat. Parameter sensitivity in rank-biased precision. In The Proceedings of the Thirteenth Australasian Document Computing Symposium, 2008. [ bib \| .pdf \| Abstract ]

Publications for 2007

[1]	Yong Zhen Guo, Kotagiri Ramamohanarao, and Laurence A. F. Park. Personalized pagerank for web page prediction based on access time-length and frequency. In The Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, pages 687--690. IEEE Computer Society, November 2007. [ bib \| DOI \| Abstract ]
[2]	Laurence A. F. Park and Kotagiri Ramamohanarao. Mining web multi-resolution community-based popularity for information retrieval. In The Proceedings of the 2007 ACM Conference on Information and Knowledge Management, pages 545--552, November 2007. [ bib \| DOI \| Abstract ]
[3]	Laurence A. F. Park and Kotagiri Ramamohanarao. Query expansion using a collection dependent probabilistic latent semantic thesaurus. In Zhi-Hua Zhou, Hang Li, and Qiang Yang, editors, The Eleventh Pacific-Asia Conference on Knowledge Discovery and Data Mining Workshop, volume 4426 of Lecture Notes in Computer Science, pages 224--235. Springer, 2007. [ bib \| DOI \| Abstract ]
[4]	Laurence A. F. Park and Yuye Zhang. On the distribution of user persistence for rank-biased precision. In The Proceedings of the Twelfth Australasian Document Computing Symposium, 2007. [ bib \| .pdf \| Abstract ]

Publications for 2005

[1]	Kotagiri Ramamohanarao and Laurence A. F. Park. Broadening vector space schemes for improving the quality of information retrieval. In The Proceedings of the Seventh Asia Pacific Web Conference, pages 15--26, March 2005. [ bib \| DOI \| Abstract ]
[2]	Laurence A. F. Park, Kotagiri Ramamohanarao, and Marimuthu Palaniswami. A novel document retrieval method using the discrete wavelet transform. ACM Transactions on Information Systems, 23(3):267--298, 2005. [ bib \| DOI \| Abstract ]
[3]	Laurence A. F. Park, Marimuthu Palaniswami, and Kotagiri Ramamohanarao. A novel document ranking method using the discrete cosine transform. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(1):130--135, January 2005. [ bib \| DOI \| Abstract ]

Publications for 2004

[1]	Kotagiri Ramamohanarao and Laurence A. F. Park. Spectral-based document retrieval. In Advances in Computer Science - ASIAN 2004, volume 3321 of Lecture Notes in Computer Science, pages 407--417. Springer Berlin / Heidelberg, December 2004. [ bib \| DOI ]
[2]	Laurence A. F. Park and Kotagiri Ramamohanarao. Hybrid pre-query term expansion using latent semantic analysis. In Rajeev Rastogi, Katharina Morik, Max Bramer, and Xindong Wu, editors, The Fourth IEEE International Conference on Data Mining, pages 178--185, Los Alamitos, California, November 2004. IEEE Computer Society. [ bib \| DOI \| Abstract ]
[3]	Laurence A. F. Park, Kotagiri Ramamohanarao, and Marimuthu Palaniswami. Fourier domain scoring : A novel document ranking method. IEEE Transactions on Knowledge and Data Engineering, 16(5):529--539, May 2004. [ bib \| DOI \| Abstract ]
[4]	Laurence A. F. Park and Kotagiri Ramamohanarao. Preliminary work on pre-query term expansion using latent semantic analysis. In The Eighth Pacific-Asia Conference on Knowledge Discovery and Data Mining Workshop, pages 1--10. Springer, Sydney, Australia, May 2004. [ bib ]
[5]	Laurence A. F. Park. Spectral Based Information Retrieval. PhD thesis, The University of Melbourne, Australia, 2004. [ bib \| Abstract ]

Publications for 2002

[1]

Laurence A. F. Park, Kotagiri Ramamohanarao, and Marimuthu Palaniswami. A new implementation technique for fast spectral based document retrieval systems. In Vipin Kumar and Shusaku Tsumoto, editors, The Second IEEE International Conference on Data Mining, pages 346--353, Los Alamitos, California, USA, December 2002. IEEE Computer Society. [ bib | DOI | Abstract ]

[2]

Laurence A. F. Park, Marimuthu Palaniswami, and Kotagiri Ramamohanarao. A novel web text mining method using the discrete cosine transform. In T. Elomaa, H. Mannila, and H. Toivonen, editors, The 6th European Conference on Principles of Data Mining and Knowledge Discovery, number 2431 in Lecture Notes in Artificial Intelligence, pages 385--396, Berlin, August 2002. Springer-Verlag. [ bib | DOI | .pdf | Abstract ]

Publications for 2001

[1]

Laurence A. F. Park, Marimuthu Palaniswami, and Ramamohanarao Kotagiri. Internet document filtering using Fourier domain scoring. In Luc de Raedt and Arno Siebes, editors, Principles of Data Mining and Knowledge Discovery, number 2168 in Lecture Notes in Artificial Intelligence, pages 362--373. Springer-Verlag, September 2001. [ bib | DOI | .pdf | Abstract ]

Selected Presentations

Implementations

textIR - a topic model software suite, used in many of my papers
Ashur - The first spectral based web search engine

Evidence of Graduation

Now Recruiting

Tue, 14 Jan 2020 11:25:05 -0400

I am now recruiting students willing to undertake a Masters or PhD degree in the fields of Information Retrieval, Data Mining or Machine Learning. Interested students should contact me and provide a current resume outlining relevant academic and professional history.

Contact

Mon, 01 Jan 0001 00:00:00 +0000

Laurence is based in the Centre for Research in Mathematics and Data Science at Western Sydney University.

Name: Assoc. Prof. Laurence A. F. Park
Department: Artificial Intelligence and Data Science.
Address: School of Computer, Data and Mathematical Sciences, Western Sydney University, Locked Bag 1797, Penrith, New South Wales 2751, Australia
Phone: +61 2 9685 9065 (Work)
Room: EN.1.32 Parramatta Campus
Email:

Laurence is also found at

Photo courtesy of Rami Mukhtar 2003.

How Confident Is Your Multi-Label Classifier? Estimating Expected Accuracy from Label Distributions

Wed, 01 Jan 2025 00:00:00 +0000

When a multi-label classifier makes a prediction — say, flagging a patient record for Diabetes, Hypertension, and COVID-19 — how confident should you be? This question is harder than it looks. In a single-label setting, the probability score attached to a prediction is a straightforward measure of confidence. In the multi-label world, it gets complicated fast.

A new paper by Laurence A. F. Park (Western Sydney University) and Jesse Read (École Polytechnique) takes a rigorous look at this problem, testing seven candidate functions for estimating expected accuracy from a multi-label probability distribution — and finding clear winners depending on how accuracy is measured.

Data Science Research Group

Tue, 14 Jan 2020 11:25:05 -0400

If you have a new idea that will advance the field of data science, why not present your idea in one of our Data Science research group seminars. If you are interested, contact me to get the process started.

textIR 0.5 Released

Thu, 14 Apr 2011 11:25:05 -0400

The latest version of textIR has been released. The new features include indexing, retrieval and topic modelling of text document sets. More information can be found at https://www.scm.uws.edu.au/~lapark/textIR.

Approximate clustering of very large scale data

Mon, 01 Jan 0001 00:00:00 +0000

Many machine learning, data mining and statistical analysis tasks are used to identify properties of data, or to build models of sampled data. Unfortunately, these analytical methods are computationally expensive; their computational resource use being a function of the dimensionality and sample count of the data being analysed. Recent advances is data acquisition (such as genetic sequencing) have allowed us to capture very large amounts of data (of the order of terabytes) for analysis and modelling. The complexity of current machine learning and data mining methods makes them infeasible to be directly applied to such large scale data.

Automatic thesaurus construction using non-linear term relationships

Mon, 01 Jan 0001 00:00:00 +0000

Text based information retrieval systems retrieve documents based on the set of key terms provided to them. The documents returned are ranked according to the count of each query term, therefore if the query terms do not exist in the document it is not found. Latent semantic analysis (LSA) is a method of computing hidden topics within documents using linear algebra. By obtaining the relationships between each hidden topic and each term, we are able to compute which terms are similar by comparing the similarity of each of the terms topics. This hidden topic information allows the retrieval system to return documents that do not contain the query terms, but do contain terms that are similar to the query terms (shown in Fig. 5). The current linear algebraic techniques use the Euclidean distance as a similarity measure for vectors. Unfortunately, the Euclidean distance is not a useful metric for term or document similarity.

Discovering document model deficiencies for information retrieval

Mon, 01 Jan 0001 00:00:00 +0000

Text based information retrieval systems are built using document models. To analyse the retrieval precision of a model, a set of queries are provided to the model and the results are compared to the desired results. This type of analysis allows us to compare the precision of different retrieval models, but it does not provide us with any feedback on where the models could be improved. Currently there are no methods of analysis of text retrieval systems that are able to show where deficiencies lie within the document model. We aim to investigate methods of retrieval analysis that is able to reveal where specific document model deficiencies occur, using a given query and document set. Our analysis will allow information retrieval experiments to be more thorough and show why certain document models achieve a certain precision, thus allowing the document models to be adjusted and improved.

Efficient and effective use of Language Models for Information Retrieval

Mon, 01 Jan 0001 00:00:00 +0000

Language models for text based information retrieval have become a de facto standard due to their simplicity and effectiveness. Recently, several language modelling techniques have been developed that assume a hidden distribution of topics within the set of documents. Such methods include Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA), where the former uses a multinomial distribution of topics, while the latter uses a Dirichlet prior. By using this notion of hidden topics, we are able to compute relationships from term to topic and hence term to term. Unfortunately, these language modelling methods produces large amounts of data and require lengthy periods of time to perform document retrieval.

Improving the quality of Web information retrieval using multi-resolution link analysis

Mon, 01 Jan 0001 00:00:00 +0000

The World Wide Web is the most important information source in modern society. The information within the Web is accessed through Web search engines such as Google and Yahoo. These search engines use a global popularity rank during the computation of their results, based on links from all of the pages on the Web. This global popularity rank used by Google is known as PageRank. Using these global popularity ranks biases the search results towards the globally popular Web pages. Our aim is to investigate the effect of a peer popularity rank where popularity is measured only through sites of a similar nature. We perform this task by observing the interaction between Web pages at a finer resolution (as shown in Fig. 1). We expect that using the peer popularity rank will allow search engines to locate Web pages that are more specific to the user needs rather than pages of general popularity. This will improve the quality of information that is delivered by Web search engines and provide organisation amongst the billions of existing Web pages.

Relevance-based document models for Information Retrieval

Mon, 01 Jan 0001 00:00:00 +0000

Document models are used for information retrieval in order to compute the probability of a query being related to the document. The majority of document models are functions of the terms that appear within the document. This implies that a query is only relevant to a document if the query terms exist within the document, which is far from the truth.

In our project, we have created a new form of document model called, a Relevance-based document model, which is built based on the relevance of each query to the document and not the words that appear within the document. Relevance-based document models are constructed using a set of queries and the associated relevance of the query to the document (shown in Fig. 6). This information allows us to construct a model that provides 100% precision results for the known queries and large improvements in precision over document content-based models for partially known queries. Since the relevance-based document models are based only on the query terms and their relevance to a document, the time required to build the models is very fast and the storage required to store the model is very compact.

Web page prefetching using temporal link analysis

Mon, 01 Jan 0001 00:00:00 +0000

Web searching should be as simple as providing the search engine with a query and having the search engine return a link to the desired Web page. Unfortunately, current Web search engines use text based queries and therefore require the user to provide keywords. Converting the users information need into a few key words is not a simple process. Due to this, Web search patterns involve the user visiting many Web pages that do not satisfy their information need, while interleaving this process with several visits to the Web search engine. Rather than the user actively searching for pages using key words, the search engine could provide pages to the user based on their Web usage patterns. By examining the users Web history, we will be able to compute the types of Web pages the user desires and hence provide search results to the user without the need for key words. This method of passive Web searching is called prefetching. Given that Google has had such success in using link analysis to provide useful retrieval results, we aim to investigate the utility of Web links to perform prefetching. To date, we have successfully applied eigenvalue analysis to our temporal link graph (shown in Fig. 3) to obtain a measure of importance for each page. We will be examining the effect of using Hidden Markov models and their benefit over the eigenvalue analysis.

A temporal link graph showing the time spent on each page (shown in each circle) and the portion of time from users that have followed each of the incoming links (shown on each of the directed edges).