ORCID Profile
0000-0002-0704-4968
Current Organisation
Monash University
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Applied Statistics | Nanomaterials | Pattern Recognition and Data Mining | Nanotechnology not elsewhere classified | Interdisciplinary Engineering not elsewhere classified | Nanotechnology | Artificial Intelligence and Image Processing
ANZSRC Socio-Economic Objective (SEO)
Industrial Chemicals and Related Products not elsewhere classified | Expanding Knowledge in the Information and Computing Sciences | Manufacturing not elsewhere classified | Expanding Knowledge in Technology |
Related Links
Publications
Beating Human Analysts in Nowcasting Corporate Earnings by using Publicly Available Stock Price and Correlation Features
Publisher: Society for Industrial and Applied Mathematics
Date: 28-04-2014
Listing closed sets of strongly accessible set systems with applications to data mining
Publisher: Elsevier BV
Date: 2010
Subgroup Discovery for Election Analysis: A Case Study in Descriptive Data Mining
Publisher: Springer Berlin Heidelberg
Date: 2010
Efficient discovery of interesting patterns based on strong closedness
Publisher: Wiley
Date: 19-11-2009
DOI: 10.1002/SAM.10057
Abstract: Finding patterns that are interesting to a user in a certain application context is one of the central goals of data mining research. Regarding all patterns above a certain frequency threshold as interesting is one way of defining interestingness. In this paper, however, we argue that in many applications, a different notion of interestingness is required in order to be able to capture “long”, and thus particularly informative, patterns that are correspondingly of low frequency. To identify such patterns, our proposed measure of interestingness is based on the degree or strength of closedness of the patterns. We show that (i) indeed this definition selects long interesting patterns that are difficult to identify with frequency‐based approaches, and (ii) that it selects patterns that are robust against noise and/or dynamic changes. We prove that the family of interesting patterns proposed here forms a closure system and use the corresponding closure operator to design a mining algorithm listing these patterns in amortized quadratic time. In particular, for nonsparse datasets its time complexity is \\documentclass{article}\\usepackage{amsmath}\\usepackage{amssymb}\\usepackage{amsbsy}\\usepackage{amsfonts}\\pagestyle{empty}\\begin{document}$\\newcommand{\\bigo}[1]{O\\!\\left(#1\\right)} \\bigo{nm}$\\end{document} per pattern, where n denotes the number of items and m the size of the database. This is equal to the best known time bound for listing ordinary closed frequent sets, which is a special case of our problem. We also report empirical results with real‐world datasets. Copyright © 2009 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 2: 346‐360, 2009
Discovering robustly connected subgraphs with simple descriptions
Publisher: IEEE
Date: 11-2019
Identifying consistent statements about numerical data with dispersion-corrected subgroup discovery
Publisher: Springer Science and Business Media LLC
Date: 28-06-2017
A machine learning approach for accelerated design of magnesium alloys. Part B: Regression and property prediction
Publisher: Elsevier BV
Date: 10-2023
One click mining-interactive local pattern discovery through implicit preference and performance learning
Publisher: ACM
Date: 11-08-2013
Beating Human Analysts in Nowcasting Corporate Earnings by Using Publicly Available Stock Price and Correlation Features
Publisher: IEEE
Date: 12-2013
A machine learning approach for accelerated design of magnesium alloys. Part A: Alloy data and property space
Publisher: Elsevier BV
Date: 10-2023
Linear space direct pattern sampling using coupling from the past
Publisher: ACM
Date: 12-08-2012
Communication-efficient distributed online prediction by dynamic model synchronization
Publisher: Springer Berlin Heidelberg
Date: 2014
Identifying domains of applicability of machine learning models for materials science
Publisher: Springer Science and Business Media LLC
Date: 04-09-2020
DOI: 10.1038/S41467-020-17112-9
Abstract: Although machine learning (ML) models promise to substantially accelerate the discovery of novel materials, their performance is often still insufficient to draw reliable conclusions. Improved ML models are therefore actively researched, but their design is currently guided mainly by monitoring the average model test error. This can render different models indistinguishable although their performance differs substantially across materials, or it can make a model appear generally insufficient while it actually works well in specific sub-domains. Here, we present a method, based on subgroup discovery, for detecting domains of applicability (DA) of models within a materials class. The utility of this approach is demonstrated by analyzing three state-of-the-art ML models for predicting the formation energy of transparent conducting oxides. We find that, despite having a mutually indistinguishable and unsatisfactory average error, the models have DAs with distinctive features and notably improved performance.
Approximating the number of frequent sets in dense data
Publisher: Springer Science and Business Media LLC
Date: 19-05-2009
Interesting pattern mining in multi-relational data
Publisher: Springer Science and Business Media LLC
Date: 14-05-2014
Direct local pattern sampling by efficient two-step random procedures
Publisher: ACM
Date: 21-08-2011
A randomized approach for approximating the number of frequent sets
Publisher: IEEE
Date: 12-2008
DOI: 10.1109/ICDM.2008.85
Interpretable Machine Learning Models for Phase Prediction in Polymerization-Induced Self-Assembly
Publisher: American Chemical Society (ACS)
Date: 19-05-2023
Discovering reliable approximate functional dependencies
Publisher: ACM
Date: 04-08-2017
Non-redundant subgroup discovery using a closure system
Publisher: Springer Berlin Heidelberg
Date: 2009
Communication-efficient distributed online learning with kernels
Publisher: Springer International Publishing
Date: 2016
Mining interesting patterns in multi-relational data with N-ary relationships
Publisher: Springer Berlin Heidelberg
Date: 2013
Space-time resolved inference-based whole-brain neurophysiological mechanism imaging: application to resting-state alpha rhythm
Publisher: Cold Spring Harbor Laboratory
Date: 04-05-2022
DOI: 10.1101/2022.05.03.490402
Abstract: Neural mechanisms are complex and difficult to image. This paper presents a new space-time resolved whole-brain imaging framework, called Neurophysiological Mechanism Imaging (NMI), that identifies neurophysiological mechanisms within cerebral cortex at the macroscopic scale. By fitting neural mass models to electromagnetic source imaging data using a novel nonlinear inference method, population averaged membrane potentials and synaptic connection strengths are efficiently and accurately imaged across the whole brain at a resolution afforded by source imaging. The efficiency of the framework enables return of the augmented source imaging results overnight using high performance computing. This suggests it can be used as a practical and novel imaging tool. To demonstrate the framework, it has been applied to resting-state magnetoencephalographic source estimates. The results suggest that endogenous inputs to cingulate, occipital, and inferior frontal cortex are essential modulators of resting-state alpha power. Moreover, endogenous input and inhibitory and excitatory neural populations play varied roles in mediating alpha power in different resting-state sub-networks. The framework can be applied to arbitrary neural mass models and has broad applicability to image neural mechanisms in different brain states. The whole-brain imaging framework can disclose the neurophysiological substrates of complicated brain functions in a spatiotemporal manner. Developed a semi-analytical Kalman filter to estimate neurophysiological variables in the nonlinear neural mass model efficiently and accurately from large-scale electromagnetic time-series. The semi-analytical Kalman filter is 7.5 times faster and 5% more accurate in estimating model parameters than the unscented Kalman filter. Provided several group-level statistical observations based on neurophysiological variables and visualised them in a whole-brain manner to show different perspectives of neurophysiological mechanisms. Applied the framework to study resting-state alpha oscillation and found novel relationships between local neurophysiological variables in specific brain regions and alpha power.
Privacy-preserving mobility monitoring using sketches of stationary sensor readings
Publisher: Springer Berlin Heidelberg
Date: 2013
Discovering reliable correlations in categorical data
Publisher: IEEE
Date: 11-2019
Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms
Publisher: IEEE
Date: 11-2018
On the Complexity of Constraint-Based Theory Extraction
Publisher: Springer Berlin Heidelberg
Date: 2009
Uncovering structure-property relationships of materials by subgroup discovery
Publisher: IOP Publishing
Date: 25-01-2017
Efficiently discovering locally exceptional yet globally representative subgroups
Publisher: IEEE
Date: 11-2017
DOI: 10.1109/ICDM.2017.29
Related Organisations
Related Funding Activities
Discovery Projects - Grant ID: DP210100045
Start Date: 07-2021
End Date: 07-2024
Amount: $450,605.00
Funder: Australian Research Council
View Funded ActivityIndustrial Transformation Research Hubs - Grant ID: IH210100025
Start Date: 09-2022
End Date: 09-2027
Amount: $4,379,165.00
Funder: Australian Research Council
View Funded Activity