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3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Zhu, Erzhou Zhu, Binbin FengLiu Anhui Univ Sch Comp Sci & Technol Hefei Anhui Peoples R China

ISBN: (纸本)9781538684818

Cluster validity index (CVI) is an important method for evaluating the effect of clustering results generated by clustering algorithms. Currently, many CVIs have proposed, but they are suffering from issues of unstable and narrow range of applications. Therefore, a new clustering validity index-NCVI, is proposed in this paper. Firstly, the NCVI index combines the idea of maximum spanning tree and Euclidean distance formula. The clustering results are obtained by using the average of the sum of the weights of the maximum spanning trees of each cluster and the minimum distance between the clusters using the Euclidean distance between each cluster center point. At the same time, based on the underlying algorithm (average link hierarchical clustering algorithm) to determine the optimal cluster number, combined with the new cluster validity index NCVI designed a new K value optimization algorithm (KVOA). Finally, the paper evaluates the validity of the newly proposed index (NCVI) through four simulation data sets and two UCI real data sets, and compares it with other six classical indicators. The experimental results show that the proposed index has a good performance advantage over other indexes in the tested data set.

关键词： clustering validity index hierarchical clustering algorithm optimal clustering number

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Energy-efficient modified LEACH protocol for IoT application

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IET WIRELESS SENSOR SYSTEMS 2018年第5期8卷 223-228页

作者： Behera, Trupti Mayee Samal, Umesh Chandra Mohapatra, Sushanta Kumar KIIT Univ Sch Elect Engn Bhubaneswar Odisha India

Wireless sensor networks (WSNs) have a wide range of applications in various fields. One of the most recent emerging applications are in the world of Internet of Things (IoT), which allows inter-connection of different objects or devices through the Internet. However, limited battery power is the major concern of WSNs as compared to mobile ad-hoc network, which affects the longevity of the network. Hence, a lot of research has been focused on to minimise the energy consumption of the WSNs. Designing of a hierarchical clustering algorithm is one of the numerous approaches to minimise the energy of the WSNs. In this study, the existing low-energy adaptive clustering hierarchy (LEACH) clustering protocol is modified by introducing a threshold limit for cluster head selection with simultaneously switching the power level between the nodes. The proposed modified LEACH protocol outperforms as compared to the existing LEACH protocol with 67% rise in throughput and extending the number of alive nodes to 1750 rounds which can be used to enhance the WSN lifetime. When compared with other energy efficient protocols, it is found that the proposed algorithm performs better in terms of stability period and network lifetime in different scenarios of area, energy and node density.

关键词： wireless sensor networks energy conservation Internet of Things access protocols telecommunication power management battery power energy consumption hierarchical clustering algorithm cluster head selection energy efficient protocols IoT application wireless sensor networks WSN low-energy adaptive clustering hierarchy LEACH protocol Internet of Things network lifetime

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Using clustering approaches for response time aware job scheduling model for internet of things (IoT)

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International Journal of Information Technology (Singapore) 2017年第2期9卷 177-195页

作者： Kumar, Sumit Raza, Zahid School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India

Internet of things (IoT) is the next evolution of internet connecting and transferring information between smart things/objects covering daily aspects of life. This is realized with the involvement of large number of sensor nodes which monitors, generates and enables aggregation of data. In such an environment, clustering becomes essential by grouping a structure of objects with similar attributes. clustering, helps in establishing topologies which in turn can be used for optimizing the quality of service (QoS) parameters while managing the resources in the underlying dynamic and heterogeneous IoT network environment. This work proposes to study and compare K-means, hierarchical clustering and fuzzy C-means clustering (FCM) algorithms to design a response time aware scheduling model for IoT. The work intends to improve the QoS by routing the data through clusters formed using the above three algorithms to observe the effect of clustering on the response time aiming to minimize the same. Establishing the clustering scheme with optimum response time results in optimizing the scheduling performance of the underlying network too by minimizing the overall execution cost. The effect on message scheduling to account for the prioritized message delivery has been studied. Simulation study proves the efficiency of the K-means clustering approach under various test conditions. © 2017, Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： clustering Fuzzy C-means clustering (FCM) algorithm hierarchical clustering algorithm Internet of things (IoT) K-means algorithm Message scheduling Response time

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Functional connectivity based parcellation of the human medial temporal lobe

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NEUROBIOLOGY OF LEARNING AND MEMORY 2016年第PartA期134卷 123-134页

作者： Wang, Shao-Fang Ritchey, Maureen Libby, Laura A. Ranganath, Charan Univ Calif Davis Ctr Neurosci Davis CA 95618 USA Univ Calif Davis Dept Psychol Davis CA 95616 USA

Regional differences in large-scale connectivity have been proposed to underlie functional specialization along the anterior-posterior axis of the medial temporal lobe (MTL), including the hippocampus (HC) and the parahippocampal gyrus (PHG). However, it is unknown whether functional connectivity (FC) can be used reliably to parcellate the human MTL. The current study aimed to differentiate subregions of the HC and the PHG based on patterns of whole-brain intrinsic FC. FC maps were calculated for each slice along the longitudinal axis of the PHG and the HC. A hierarchical clustering algorithm was then applied to these data in order to group slices according to the similarity of their connectivity patterns. Surprisingly, three discrete clusters were identified in the PHG. Two clusters corresponded to the parahippocampal cortex (PHC) and the perirhinal cortex (PRC), and these regions showed preferential connectivity with previously described posterior-medial and anterior-temporal networks, respectively. The third cluster corresponded to an anterior PRC region previously described as area 36d, and this region exhibited preferential connectivity with auditory cortical areas and with a network involved in visceral processing. The three PHG clusters showed different profiles of activation during a memory-encoding task, demonstrating that the FC-based parcellation identified functionally dissociable sub-regions of the PHG. In the hippocampus, no sub-regions were identified via the parcellation procedure. These results indicate that connectivity-based methods can be used to parcellate functional regions within the MTL, and they suggest that studies of memory and high-level cognition need to differentiate between PHC, posterior PRC, and anterior PRC. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Functional connectivity hierarchical clustering algorithm Parcellation Hippocampus Parahippocampal gyrus

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Enhancing UML Class Diagram Abstraction with Knowledge Graph 17th

Enhancing UML Class Diagram Abstraction with Knowledge Graph

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17th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

作者： Huang, Liang Duan, Yucong Sun, Xiaobing Lin, Zhaoxin Zhu, Chuanpu Hainan Univ Coll Informat Sci & Technol Haikou Peoples R China Yangzhou Univ Sch Informat Engn Yangzhou Jiangsu Peoples R China Iowa State Univ Sch Business Ames IA USA

ISBN: (纸本)9783319462578;9783319462561

Model-Driven Engineering (MDE) alleviates the cognitive complexity and effort spent on software development by generating codes from models. In MDE, models should be accurate, refined, reliable and efficient. Class diagram is a structural abstraction of a real system and usually used in software design. A better designed class diagram could lead to a better system. In this paper, we proposed a knowledge graph based method to improve class diagrams. We took knowledge graph as the media layer for easier information introduction, and proposed methods to map data, information and knowledge between class diagrams and knowledge graphs bidirectionally. Based on the added knowledge source, we designed hierarchical clustering algorithm to abstract the class diagram, and finally we generated abstracted class diagrams automatically.

关键词： Class diagram Knowledge graph UML OCL hierarchical clustering algorithm

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A fast hierarchical clustering algorithm for functional modules discovery in protein interaction networks

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IEEE/ACM transactions on computational biology and bioinformatics 2011年第3期8卷 607-20页

作者： Jianxin Wang Min Li Jianer Chen Yi Pan Department of Computer Science School of Information Science and Engineering Central South University Changsha 410083 China. jxwang@***

As advances in the technologies of predicting protein interactions, huge data sets portrayed as networks have been available. Identification of functional modules from such networks is crucial for understanding principles of cellular organization and functions. However, protein interaction data produced by high-throughput experiments are generally associated with high false positives, which makes it difficult to identify functional modules accurately. In this paper, we propose a fast hierarchical clustering algorithm HC-PIN based on the local metric of edge clustering value which can be used both in the unweighted network and in the weighted network. The proposed algorithm HC-PIN is applied to the yeast protein interaction network, and the identified modules are validated by all the three types of Gene Ontology (GO) Terms: Biological Process, Molecular Function, and Cellular Component. The experimental results show that HC-PIN is not only robust to false positives, but also can discover the functional modules with low density. The identified modules are statistically significant in terms of three types of GO annotations. Moreover, HC-PIN can uncover the hierarchical organization of functional modules with the variation of its parameter's value, which is approximatively corresponding to the hierarchical structure of GO annotations. Compared to other previous competing algorithms, our algorithm HC-PIN is faster and more accurate.

关键词： Protein Interaction Network Functional Module hierarchical clustering algorithm Gene Ontology

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A Novel Method for Online Bursty Event Detection on Twitter 6

A Novel Method for Online Bursty Event Detection on Twitter

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6th IEEE International Conference on Software Engineering and Service Science (ICSESS)

作者： Zhang, Yu Qu, Zhiyi Lanzhou Univ Sch Informat Sci & Engn Lanzhou Gansu Peoples R China

ISBN: (纸本)9781479983537

As one of the most popular social media platforms, twitter has become a tool that people widely used to share their contents, their interests and events with friends. Meanwhile, we are facing a big challenge to find the bursty events from the large volume of continuous text streams quickly and accurately due to millions of data produced every day. In this paper, we proposed a BBW (Basic-Burst Weight) method based on the Time Window to extract bursty words, then we exploit these bursty words to detect the meaningful bursty events combined with hierarchical clustering algorithm. Our experiments on a large twitter dataset show that our method can detect bursty events timely and precisely.

关键词： Twitter time window burst words hierarchical clustering algorithm

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Learning of hierarchical Fuzzy Aggregative Network Using Simplified Swarm Optimization

Learning of Hierarchical Fuzzy Aggregative Network Using Sim...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Wei, Shang-Chia Yen, Tso-Jung Yeh, Wei-Chang Acad Sinica Inst Stat Sci Taipei 11529 Taiwan Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu 300 Taiwan

ISBN: (纸本)9781479974924

hierarchical fuzzy aggregation network (HFAN) is a fine multilayer information fusion system that carries out multi-criteria aggregation. It can be regarded as a functional classifier for dealing with decision-making problems. The HFAN comprises fuzzy aggregation operators built from adjusted parameters (gamma) and associated weights (delta). In this paper, we adopt soft computing techniques (e.g., PSO and SSO) to learn these fuzzy aggregation operators. We provide association rules to define input data and use a hierarchical clustering algorithm to organize the network structure. The optimization efficiency of these rules is experimented with different network topologies and datasets. We verify effectiveness of HFAN by applying it to classify the breast cancer dataset from the UCI Machine Learning Repository. We conduct study for comparing the optimized HFAN and other approaches in terms of ten-fold cross-validation.

关键词： hierarchical Fuzzy Aggregation Network Soft Computing Associattion Rules hierarchical clustering algorithm Breast Cancer dataset ten-fold cross-validation

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A Novel Method for Online Bursty Event Detection on Twitter

A Novel Method for Online Bursty Event Detection on Twitter

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2015 6th IEEE International Conference on Software Engineering and Service Science(ICSESS 2015)

作者： Yu Zhang Zhiyi Qu School of Information Science and Engineering Lanzhou University

As one of the most popular social media platforms,twitter has become a tool that people widely used to share their contents, their interests and events with friends. Meanwhile, we are facing a big challenge to find the bursty events from the large volume of continuous text streams quickly and accurately due to millions of data produced every day. In this paper, we proposed a BBW（Basic-Burst Weight） method based on the Time Window to extract bursty words, then we exploit these bursty words to detect the meaningful bursty events combined with hierarchical clustering algorithm. Our experiments on a large twitter dataset show that our method can detect bursty events timely and precisely.

关键词： Twitter time window burst words hierarchical clustering algorithm

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Interference modelling using hierarchical spatial clustering of terrain and user density maps 79

Interference modelling using hierarchical spatial clustering...

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IEEE 79th Vehicular Technology Conference (VTC-Spring)

作者： Koufos, Konstantinos Jaentti, Riku

ISBN: (纸本)9781479944828

In environments with non-uniform user density and correlated slow fading, the Poisson Point Process (PPP) model fails to capture the distribution of aggregate interference. One way to overcome this issue is to divide the deployment area into multiple disjoint clusters and describe the interference from each cluster using a PPP with cluster-specific PPP intensity and pathloss model. Obviously, too few clusters do not describe the distribution of aggregate interference accurately, while on the other hand, too many clusters incur high computational and storage overheads. In this direction, we propose to divide the area according to a hierarchical clustering algorithm which uses the Akaike Information Criterion to balance between complexity and accuracy. The algorithm can be useful for database-assisted spectrum access. It allows a database to assess the interference between systems (overlapping in spectrum but not in space) in a low-complexity manner.

关键词： inference mechanisms stochastic processes telecommunication traffic terrain mapping Akaike information criterion Poisson point process correlated slow fading hierarchical clustering algorithm hierarchical spatial clustering interference modelling user density maps Aggregates Complexity theory Correlation Equations Fading Interference Mathematical model Inference mechanisms Complexity theory terrain mapping telecommunication traffic clustering algorithms interference Markov chain fading color Akaike Information Criteria (AIC) Mathematical Model interference point operation Public-Private Sector Partnerships Point-to-Point Protocol terrains

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