Browsing by Author "Zhou J"
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- ItemCombining data from consumers and traditional medicine practitioners to provide a more complete picture of Chinese bear bile markets(John Wiley and Sons Ltd on behalf of British Ecological Society, 2021-10-07) Hinsley A; Hu S; Chen H; Garshelis D; Hoffmann M; Lee TM; Moyle B; Qiu Y; Ruan X; Wan AKY; Zhou J; Milner-Gulland EJ; Friant S1. Understanding wildlife consumption is essential for the design and evaluation of effective conservation interventions to reduce illegal trade. This requires understanding both the consumers themselves and those who influence their behaviour. For example, in markets for wildlife-based medicines, both consumers and medical practitioners have a role in which products are consumed. 2. We used mixed methods to triangulate data on bear bile consumption from 3,646 members of the public, 80 pharmacy workers and 38 Traditional Chinese Medicine (TCM) doctors in four provincial capital cities across China. Bear bile can be sold legally in packaged TCM products made from farmed bile, or sold illegally, often as raw gallbladders from wild bears. We interviewed medical practitioners, and surveyed the public using both direct questions (DQ) and the Unmatched Count Technique (UCT), an indirect method used to improve reporting of sensitive behaviours. We applied a ‘combined’ UCT-DQ analysis to produce a more robust consumption estimate. 3. In all, 140 (3.8%) survey respondents directly reported recent (<3 years) bile consumption, but the combined UCT-DQ estimate was 11.2%. In total, 14 survey respondents (0.4% sample and 10% recent consumers) self-reported recent wild bile consumption. Almost a quarter of doctors and half of pharmacy workers had ever prescribed bile. 4. Around half of doctors and over a quarter of pharmacy workers said that bear bile was the best medicine in certain situations. More than half of doctors and over a third of pharmacy workers thought wild bile was more effective than farmed, although we found no evidence of wild bile being formally prescribed. Consumers could name specific treatment uses of bile but almost half of recent consumers did not know the source of bile they had consumed. 5. We show that gathering perspectives from different wildlife market actors can generate a more complete picture of trade. In China, bile consumption may be limited by its specific TCM treatment uses, but whether practitioner views on the greater effectiveness of wild bile are passed to consumers must be investigated further. With potential overlap between farmed and wild consumption, any interventions to change these markets must carefully consider how both consumers and practitioners may react.
- ItemInitialization-similarity clustering algorithm(Springer Science+Business Media, LLC, 2019-12) Liu T; Zhu J; Zhou J; Zhu Y; Zhu XClassic 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.
- ItemJoint Spectral Clustering based on Optimal Graph and Feature Selection(Springer Nature Switzerland AG, 2021-02) Zhu J; Jang-Jaccard J; Liu T; Zhou JRedundant features and outliers (noise) included in the data points for a machine learning clustering model heavily influences the discovery of more distinguished features for clustering. To solve this issue, we propose a spectral new clustering method to consider the feature selection with the L2 , 1-norm regularization as well as simultaneously learns orthogonal representations for each sample to preserve the local structures of data points. Our model also solves the issue of out-of-sample, where the training process does not output an explicit model to predict unseen data points, along with providing an efficient optimization method for the proposed objective function. Experimental results showed that our method on twelve data sets achieves the best performance compared with other similar models.
- ItemWeighted adjacent matrix for K-means clustering(Springer Science+Business Media, LLC, 2019-12) Zhou J; Liu T; Zhu JK-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.