Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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    Initialization-similarity clustering algorithm
    (Springer Science+Business Media, LLC, 2019-12) Liu T; Zhu J; Zhou J; Zhu Y; Zhu X
    Classic k-means clustering algorithm randomly selects centroids for initialization to possibly output unstable clustering results. Moreover, random initialization makes the clustering result hard to reproduce. Spectral clustering algorithm is a two-step strategy, which first generates a similarity matrix and then conducts eigenvalue decomposition on the Laplacian matrix of the similarity matrix to obtain the spectral representation. However, the goal of the first step in the spectral clustering algorithm does not guarantee the best clustering result. To address the above issues, this paper proposes an Initialization-Similarity (IS) algorithm which learns the similarity matrix and the new representation in a unified way and fixes initialization using the sum-of-norms regularization to make the clustering more robust. The experimental results on ten real-world benchmark datasets demonstrate that our IS clustering algorithm outperforms the comparison clustering algorithms in terms of three evaluation metrics for clustering algorithm including accuracy (ACC), normalized mutual information (NMI), and Purity.
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    Rentrez: An R package for the NCBI eUtils API
    (The R Foundation for Statistical Computing, 2017-12) Winter DJ
    The USA National Center for Biotechnology Information (NCBI) is one of the world's most important sources of biological information. NCBI databases like PubMed and GenBank contain millions of records describing bibliographic, genetic, genomic, and medical data. Here I present rentrez, a package which provides an R interface to 50 NCBI databases. The package is well-documented, contains an extensive suite of unit tests and has an active user base. The programmatic interface to the NCBI provided by rentrez allows researchers to query databases and download or import particular records into R sessions for subsequent analysis. The complete nature of the package, its extensive test-suite and the fact the package implements the NCBI's usage policies all make rentrez a powerful aid to developers of new packages that perform more specific tasks.
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    Exploring spiral narratives with immediate feedback in immersive virtual reality serious games for earthquake emergency training
    (1/01/2023) Feng Z; González VA; Mutch C; Amor R; Cabrera-Guerrero G
    Various attempts and approaches have been made to teach individuals about the knowledge of best practice for earthquake emergencies. Among them, Immersive Virtual Reality Serious Games (IVR SGs) have been suggested as an effective tool for emergency training. The notion of IVR SGs is consistent with the concept of problem-based gaming (PBG), where trainees interact with games in a loop of forming a playing strategy, applying the strategy, observing consequences, and making reflection. PBG triggers reflection-on-action, enabling trainees to reform perceptions and establish knowledge after making a response to a scenario. However, in the literature of PBG, little effort has been made for trainees to reflect while they are making a response (i.e., reflection-in-action) in a scenario. In addition, trainees do not have the possibility to adjust their responses and reshape their behaviors according to their reflection-in-action. In order to overcome these limitations, this study proposes a game mechanism, which integrates spiral narratives with immediate feedback, to underpin reflection-in-action and reflective redo in PBG. An IVR SG training system suited to earthquake emergency training was developed, incorporating the proposed game mechanism. A controlled experiment with 99 university students and staff was conducted. Participants were divided into three groups, with three interventions tested: a spiral narrated IVR SG, a linear narrated IVR SG, and a leaflet. Both narrated IVR SGs were effective in terms of immediate knowledge gain and self-efficacy improvement. However, challenges and opportunities for future research have been suggested.
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    Weighted adjacent matrix for K-means clustering
    (Springer Science+Business Media, LLC, 2019-12) Zhou J; Liu T; Zhu J
    K-means clustering is one of the most popular clustering algorithms and has been embedded in other clustering algorithms, e.g. the last step of spectral clustering. In this paper, we propose two techniques to improve previous k-means clustering algorithm by designing two different adjacent matrices. Extensive experiments on public UCI datasets showed the clustering results of our proposed algorithms significantly outperform three classical clustering algorithms in terms of different evaluation metrics.