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2nd International Conference on Intelligent Computing, Communication & Convergence (ICCC)

作者： Nidhi Patel, Km Archana Natl Inst Technol Kurukshetra 136119 Haryana India

Data clustering is a method of putting same data object into group. A clustering rule does partitions of a data set into many groups supported the principle of maximizing the intra-class similarity and minimizing the inter-class similarity. Finding clusters in object, particularly high dimensional object, is difficult when the clusters are different shapes, sizes, and densities, and when data contains noise and outliers. This paper provides a new clustering algorithm for normalized data set and proven that our new planned clustering approach work efficiently when dataset are normalized. (C) 2016 The Authors. Published by Elsevier B.V.

关键词： density based clustering algorithm Computational complexity

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An Efficient and Scalable density-based clustering algorithm for Normalize Data

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Procedia Computer Science 2016年 92卷 136-141页

作者： Nidhi Km Archana Patel National Institute of Technology Kurukshetra 136119 Haryana India

关键词： density based clustering algorithm Computational complexity

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A density-based clustering Approach to Motor Unit Potential Characterizations to Support Diagnosis of Neuromuscular Disorders

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2017年第7期25卷 956-966页

作者： Kamali, Tahereh Stashuk, Daniel W. Univ Waterloo Syst Design Engn Waterloo ON N2L 3G1 Canada

Electrophysiological muscle classification involves characterization of extracted motor unit potentials (MUPs) followed by the aggregation of these MUP characterizations. Existing techniques consider three classes (i.e., myopathic, neurogenic, and normal) for both MUP characterization and electrophysiological muscle classification. However, diseased-induced MUP changes are continuous in nature, which make it difficult to find distinct boundaries between normal, myopathic, and neurogenic MUPs. Hence, MUP characterization based on more than three classes is better able to represent the various effects of disease. Here, a novel, electrophysiological muscle classification system is proposed, which considers a dynamic number of classes for characterizing MUPs. To this end, a clustering algorithm called neighborhood distances entropy consistency is proposed to find clusters with arbitrary shapes and densities in an MUP feature space. These clusters represent several concepts of MUP normality and abnormality and are used for MUP characterization instead of the conventional three classes. An examined muscle is then classified by embedding its MUP characterizations in a feature vector fed to an ensemble of support vector machine and nearest neighbor classifiers. For 103 sets of MUPs recorded in tibialis anterior muscles, the proposed system had a 97% electrophysiological muscle classification accuracy, which is significantly higher than in previous works.

关键词： Chameleon DBSCAN density based clustering algorithm ensemble of SVM classifiers k-means needle electrodiagnostic examination quantitative electromyographic muscle classification spectral clustering

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Data clustering Approaches Survey and Analysis 1

Data Clustering Approaches Survey and Analysis

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International Conference On Futuristic Trends in Computational Analysis and Knowledge Management

作者： Ahalya, G. Pandey, Hari Mohan Amity Univ ASET CSE Dept Noida India

ISBN: (纸本)9781479984336

In the current world, there is a need to analyze and extract information from data. clustering is one such analytical method which involves the distribution of data into groups of identical objects. Every group is known as a cluster, which consists of objects that have affinity within the cluster and disparity with the objects in other groups. This paper is intended to examine and evaluate various data clustering algorithms. The two major categories of clustering approaches are partition and hierarchical clustering. The algorithms which are dealt here are: k-means clustering algorithm, hierarchical clustering algorithm, density based clustering algorithm, self-organizing map algorithm, and expectation maximization clustering algorithm. All the mentioned algorithms are explained and analyzed based on the factors like the size of the dataset, type of the data set, number of clusters created, quality, accuracy and performance. This paper also provides the information about the tools which are used to implement the clustering approaches. The purpose of discussing the various software/tools is to make the beginners and new researchers to understand the working, which will help them to come up with new product and approaches for the improvement.

关键词： clustering Expectation maximization clustering algorithm Hierarchical clustering K-means clustering algorithm Self-organization maps algorithm density based clustering algorithm

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PATIENT-SPECIFIC ECG CLASSIFICATION based ON RECURRENT NEURAL NETWORKS AND clustering TECHNIQUE 13

PATIENT-SPECIFIC ECG CLASSIFICATION BASED ON RECURRENT NEURA...

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13th IASTED International Conference on Biomedical Engineering (BioMed)

作者： Zhang, Chenshuang Wang, Guijin Zhao, Jingwei Gao, Pengfei Lin, Jianping Yang, Huazhong Tsinghua Univ Dept Elect Engn Beijing Peoples R China Beijing Xinheyidian Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9780889869905

In this paper, we propose a novel patient-specific electrocardiogram (ECG) classification algorithm based on the recurrent neural networks (RNN) and density based clustering technique. We use RNN to learn time correlation among ECG signal points and to classify ECG beats with different heart rates. Morphology information including the present beat and the T wave of former beat is fed into RNN to learn underlying features automatically. clustering method is employed to find representative beats as the training data. Evaluated on the MIT-BIB Arrhythmia Database, the experimental results show that proposed algorithm achieves the state-of-the-art classification performance.

关键词： ECG Classification Deep Learning Recurrent Neural Networks density based clustering algorithm

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An Efficient Scanning Approach for Multiple Targets 2

An Efficient Scanning Approach for Multiple Targets

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2nd International Conference on Intelligent Technologies and Engineering Systems (ICITES)

作者： Hsieh, Cheng-Tiao Ming Chi Univ Technol Dept Ind Design New Taipei City 24301 Taiwan

ISBN: (纸本)9783319045733;9783319045726

Kinect is a popular motion sensor often applied in an intuitive PC games. In recent years, it had been applied to construct a low cost 3D scanner. Kinect scanner is capable of generating a set of point clouds, and its outputs are usually rough and need to be treated by appropriate applications before advanced 3D applications. For this need, this paper proposed a new approach which is capable of building multiple targets from a raw point cloud captured by a Kinect scanner efficiently. The principle of extracting targets is based on Background subtraction and this method had been developed by author in 2013. This paper proposed a segment method which is used to segment a multiple target representation extracted by the previous method. The principle of segment method is based on the clustering algorithm called DBSCAN. By means of this approach, a multiple scanning process can be accomplished.

关键词： Segment Kinect density based clustering algorithm

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Identifying Equivalent Load Model of Power Systems With Wind Power Integration 11

Identifying Equivalent Load Model of Power Systems With Wind...

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11th UKACC International Conference on Control (UKACC Control)

作者： Zhu, Zi Bail, Xuefeng Zhang, Wei Liu, Ruiye Xu, Ying Harbin Inst Technol Sch Elect Engn & Automat Harbin 150001 Peoples R China State Grid Power Dispatch & Control Ctr North China Branch Beijing 100053 Peoples R China

ISBN: (纸本)9781467398916

With a large number of wind farms integrated into power systems, the load characteristics of the power systems will vary, resulting in the inapplicability of the traditional load model. Thus, it is of great importance to describe the load characteristics of power systems by taking the impact of the integration of the wind power into consideration. This paper first discusses the influence of wind power integration on the original load model. Wind speed, which has the most direct impact on the output of the wind turbine, is introduced as a new variable for load modelling. By combing the voltage variation of the system, a static equivalent load model is proposed in this paper, wherein, the structure of the proposed load model is also discussed. Measurement-based load Modelling with large amount of measurements requires to process a large number of measured data. This paper utilizes the density based clustering algorithm (DBSCAN) to mine the core data, and reconstructs the surface of the load characteristic based on the mined core data. A static equivalent load model with wind power integration is then established. Compared with the model that is modeled directly without data processing, the proposed load model is more accurate.

关键词： Load model Wind power integration density based clustering algorithm Data mining

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