THCluster: herb supplements categorization for precision traditional Chinese medicine
- Authors: Ruan, Chunyang , Wang, Ye , Zhang, Yanchun , Ma, Jiangang , Chen, Huijuan , Aickelin, Uwe , Zhu, Shanfeng , Zhang, Ting
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);Kansas City, MO, USA; 13-16 Nov. 2017 p. 417-424
- Full Text: false
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- Description: There has been a continuing demand for traditional and complementary medicine worldwide. A fundamental and important topic in Traditional Chinese Medicine (TCM) is to optimize the prescription and to detect herb regularities from TCM data. In this paper, we propose a novel clustering model to solve this general problem of herb categorization, a pivotal task of prescription optimization and herb regularities. The model utilizes Random Walks method, Bayesian rules and Expectation Maximization(EM) models to complete a clustering analysis effectively on a heterogeneous information network. We performed extensive experiments on the real-world datasets and compared our method with other algorithms and experts. Experimental results have demonstrated the effectiveness of the proposed model for discovering useful categorization of herbs and its potential clinical manifestations.
The choice of a similarity measure with respect to its sensitivity to outliers
- Authors: Rubinov, Alex , Sukhorukova, Nadezda , Ugon, Julien
- Date: 2010
- Type: Text , Journal article
- Relation: Dynamics of Continuous, Discrete and Impulsive Systems Series B: Applications and Algorithms Vol. 17, no. 5 (2010), p. 709-721
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- Description: This paper examines differences in the choice of similarity measures with respect to their sensitivity to outliers in clustering problems, formulated as mathematical programming problems. Namely, we are focusing on the study of norms (norm-based similarity measures) and convex functions of norms (function-norm-based similarity measures). The study consists of two parts: the study of theoretical models and numerical experiments. The main result of this study is a criterion for the outliers sensitivity with respect to the corresponding similarity measure. In particular, the obtained results show that the norm-based similarity measures are not sensitive to outliers whilst a very widely used square of the Euclidean norm similarity measure (least squares) is sensitive to outliers. Copyright © 2010 Watam Press.
The impact of global and local features on multiple sequence alignment clustering-based near-duplicate video retrieval
- Authors: Wang, Yandan , Lu, Guojun , Belkhatir, Mohammed , Messom, Christopher
- Date: 2013
- Type: Text , Conference paper
- Relation: 14th Pacific-Rim Conference on Multimedia p. 669-677
- Full Text: false
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- Description: Traditionally, the performance of Near-Duplicate Video Retrieval (NDVR) is enhanced through different video features, matching scheme and indexing methods. The video features have been intensively investigated and it has been shown that local features outperform global features in terms of accuracy. However, local features have the expensive computational problem. Therefore, indexing structure is introduced to assist in scaling up, whilst the accuracy will drop slightly or dramatically in most time by using indexing approaches. Recent progress shows that NDVR based on clustering could reduce searching space while maintains equivalent retrieval accuracy compared to that of non-clustering based. In this paper, we will continue to evaluate clustering based NDVR, but using popular global and local features. Before conducting NDVR, dataset will be pre-processed offline into groups by using clustering algorithm that near-duplicate videos (NDVs) are assembled in the same cluster. Each cluster will be represented by member video or the centroid. The query video will then be compared to the representative videos instead of all videos in database (non-clustering based). Our experiment shows that clustering-based NDVR using global and local features outperforms than that of non-clustering based in terms of both retrieval accuracy and speed.
Tourism clusters : Uncovering destination value chains
- Authors: Hollick, Mary , Braun, Patrice
- Date: 2006
- Type: Text , Conference paper
- Relation: Paper presented at CAUTHE 2006 conference - to the city and beyond, Melbourne, Victoria : 6th February, 2006 p. 476-485
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- Description: This paper discusses the role of tourism networks, clustering and destination value chains for micro and small and medium size tourism enterprises (SMEs) in freely assembled destinations. In discussing destination benefits and barriers surrounding SME clustering, SME positioning and performance are highlighted. It is proposed in this paper that SME clustering and value are not always naturally established. Successful destination clusters may be created by upgrading SME performance, analysing local value chains and matching both tangible and intangible sources of value, such as systems, leadership, relationships and brands with demand-side value segmentation.
- Description: E1
- Description: 2003001808
Two level clustering using SOM and dynamical systems
- Authors: Ghosh, Ranadhir , Mammadov, Musa , Ghosh, Moumita , Yearwood, John
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at ICOTA6: 6th International Conference on Optimization - Techniques and Applications, Ballarat, Victoria : 9th December, 2004
- Full Text: false
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- Description: E1
- Description: 2003000871
Unsupervised and supervised data classification via nonsmooth and global optimisation
- Authors: Bagirov, Adil , Rubinov, Alex , Sukhorukova, Nadezda , Yearwood, John
- Date: 2003
- Type: Text , Journal article
- Relation: Top Vol. 11, no. 1 (2003), p. 1-92
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- Description: We examine various methods for data clustering and data classification that are based on the minimization of the so-called cluster function and its modications. These functions are nonsmooth and nonconvex. We use Discrete Gradient methods for their local minimization. We consider also a combination of this method with the cutting angle method for global minimization. We present and discuss results of numerical experiments.
- Description: C1
- Description: 2003000421
VANET–LTE based heterogeneous vehicular clustering for driving assistance and route planning applications
- Authors: Ahmad, Iftikhar , Noor, Rafidah , Ahmedy, Ismail , Shah, Syed , Imran, Muhammad
- Date: 2018
- Type: Text , Journal article
- Relation: Computer Networks Vol. 145, no. (2018), p. 128-140
- Full Text: false
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- Description: The Internet of vehicles incorporates multiple access networks and technologies to connect vehicles on roads. These vehicles usually require the use of individual long-term evolution (LTE) connections to send/receive data to/from a remote server to make smart decisions regarding route planning and driving. An increasing number of vehicles on the roads may not only overwhelm LTE network usage but also incur added cost. Clustering helps minimize LTE usage, but the high speed of vehicles renders connections unstable and unreliable not only among vehicles but also between vehicles and the LTE network. Moreover, non-cooperative behavior among vehicles within a cluster is a bottleneck in sharing costly data acquired from the Internet. To address these issues, we propose a novel destination- and interest-aware clustering (DIAC) mechanism. DIAC primarily incorporates a strategic game-theoretic algorithm and a self-location calculation algorithm. The former allows vehicles to participate/cooperate and enforces a fair-use policy among the cluster members (CMs), whereas the latter enables CMs to calculate their location coordinates in the absence of a global positioning system under an urban topography. DIAC strives to reduce the frequency of link failures not only among vehicles but also between each vehicle and the 3G/LTE network. The mechanism also considers vehicle mobility and LTE link quality and exploits common interests among vehicles in the cluster formation phase. The performance of the DIAC mechanism is validated through extensive simulations, whose results demonstrate that the performance of the proposed mechanism is superior to that of similar and existing approaches. © 2018 Elsevier B.V.