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12th International Conference on Computing-and-Control-for-the-Water-Industry (CCWI)

作者： Sun, J. Wang, R. Wang, X. Yang, H. Ping, J. Thinghuct Univ Grad Sch Shenzhen Shenzhen 518055 Peoples R China

Urban water supply network is a hidden underground asset;therefore, it is difficult to make a direct diagnosis towards the faults of pipe network by means of the detecting technology. Based on the historical data of the rupture of water supply pipe network, a spatial cluster analysis will be carried out towards the historical data of spatial fault in water supply pipeline, using density-based cluster analysis and the dbscan algorithm. K-neighborhood graph is well-used in optimizing the input parameter radius of neighborhood (Eps) and density (MinPts) and classifying the historical data of the rupture of pipeline. According to the pipe network failure point coordinates (X, Y), adopting density-based cluster analysis, a danger level classification will be researched without knowing the reasons of the rupture of pipe network, which lays the foundation for finding out pipeline rupture in local areas in the future. Meanwhile, a noise analysis will be processed in terms of the historical data of pipeline rupture in water supply network, avoiding the ineffective data caused by recording errors and regular analysis. This research focuses on the reasons that result in the rupture of water supply network, which plays a significant role in the identification of high-density and high-danger zones in water supply networks. (C) 2013 The Authors. Published by Elsevier Ltd. Selection and peer-review under responsibility of the CCWI2013 Committee

关键词： dbscan algorithm Spatial cluster analysis Pipe burst Water supply network Data mining

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Flight Operations Quality Assurance Based on Clustering Analysis

Flight Operations Quality Assurance Based on Clustering Anal...

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1st Symposium on Aviation Maintenance and Management

作者： Sun, Zhuo Ma, Cunbao Li, Wen Shen, Chunnan Northwestern Polytech Univ Sch Aeronaut Xian 710072 Shaanxi Peoples R China

ISBN: (纸本)9783642542336;9783642542329

Flight operations quality assurance (FOQA) can be applied to monitoring and analyzing the data recorded in a flight to improve line operations and safety. In view of the requirements of FOQA and the shortage of general data processing algorithms, clustering analysis and density-based spatial clustering of applications with noise (dbscan) algorithm are studied depending on data mining, and FOQA based on clustering analysis is presented. The process of flight data analysis is discussed with this algorithm. The practicability and universality of dbscan algorithm is validated by the clustering analysis instance of an abnormal data during takeoff.

关键词： Flight operations quality assurance Clustering analysis dbscan algorithm

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Inferring Road Maps from Sparsely-Sampled GPS Traces

Inferring Road Maps from Sparsely-Sampled GPS Traces

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27th Canadian Conference on Artificial Intelligence (AI)

作者： Qiu, Jia Wang, Ruisheng Wang, Xin Univ Calgary Dept Geomat Engn Calgary AB Canada

ISBN: (纸本)9783319064833;9783319064826

In this paper, we proposed a new segmentation-and-grouping framework for road map inference from sparsely-sampled GPS traces. First, we extended dbscan with the orientation constraint to partition the whole point set of traces to clusters representing road segments. Second, we proposed an adaptive k-means algorithm that the k value is determined by an angle threshold to reconstruct nearly straight line segments. Third, the line segments are grouped according to the 'Good Continuity' principle of Gestalt Law to form a 'Stroke' for recovering the road map. Experiment results show that our algorithm is robust to noise and sampling rate. In comparison with previous work, our method has advantages to infer the road maps from sparsely-sampled GPS traces.

关键词： Map inference GPS traces dbscan algorithm k-means clustering perceptual grouping

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Density Clustering Based on Border-Expanding 10

Density Clustering Based on Border-Expanding

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10th International Conference on Natural Computation (ICNC)

作者： Chen, Dongming Yan, Yun Wang, Dongqi Northeastern Univ Software Coll Shenyang Peoples R China

ISBN: (纸本)9781479951512

dbscan is a clustering algorithm based on density. It can divide regions which have a high density for clusters, shield the noise effectively and discover clusters of arbitrary shape and any size from dataset. However, dbscan algorithm needs to traverse dataset to find core objects, so it results in large amount of I/O cost when processing large-scale datasets. A fast algorithm (BEdbscan) is developed which expands the cluster by employing border objects as seeds. Experimental results show that BEdbscan performs obvious efficiency improvement than dbscan algorithm especially when processing large datasets.

关键词： density clustering dbscan algorithm

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Spatial Cluster Analysis of Bursting Pipes in Water Supply Networks

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Procedia Engineering 2014年 70卷 1610-1618页

作者： J. Sun R. Wang X. Wang H. Yang J. Ping Graduate School at Shenzhen Tsinghua University the University Town Shenzhen 518055 China

Urban water supply network is a hidden underground asset; therefore, it is difficult to make a direct diagnosis towards the faults of pipe network by means of the detecting technology. Based on the historical data of the rupture of water supply pipe network, a spatial cluster analysis will be carried out towards the historical data of spatial fault in water supply pipeline, using density- based cluster analysis and the dbscan algorithm. K-neighborhood graph is well-used in optimizing the input parameter radius of neighborhood (Eps) and density (MinPts) and classifying the historical data of the rupture of pipeline. According to the pipe network failure point coordinates (X,Y), adopting density-based cluster analysis, a danger level classification will be researched without knowing the reasons of the rupture of pipe network, which lays the foundation for finding out pipeline rupture in local areas in the future. Meanwhile, a noise analysis will be processed in terms of the historical data of pipeline rupture in water supply network, avoiding the ineffective data caused by recording errors and regular analysis. This research focuses on the reasons that result in the rupture of water supply network, which plays a significant role in the identification of high-density and high-danger zones in water supply networks.

关键词： dbscan algorithm Spatial cluster analysis Pipe burst Water supply network Data mining

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Memory Effect in dbscan algorithm

Memory Effect in DBSCAN Algorithm

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4th International Conference on Computer Science and Education

作者： Li Jian Yu Wei Yan Bao-Ping Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China Bei Hang Univ Software Coll Beijing Peoples R China

ISBN: (纸本)9781424435197

As a density-based clustering algorithm, dbscan plays an important role in data mining. Normally dbscan algorithm is computationally expensive, limiting its performance in large scale data sets, especially in high dimensional data sets. The high complexity is rooted from the region queries, a very common operation in density-based algorithms, which brings the complexity of the algorithms to O(n(2)), where a is the number of database objects. With the help of index structure the complexity can be reduced to O(nlogn), however it is inefficient to create the index structureespecially for high dimensional data sets or large scale databases. In this paper we propose a new concept named memory effect (ME). ME can be used to shrink the scope of region queries to neighboring objects. Based on ME we have improved dbscan algorithm evidently, and empirical experiments have shown the improvement in both effectiveness and efficiency. At last, we give the theoretical analysis of MEdbscan algorithm and talk about the influence of parameters.

关键词： Density-based clustering dbscan algorithm MEdbscan algorithm Memory Effect

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A Multi-Agent Vehicle Routing System for Garbage Collection 5

A Multi-Agent Vehicle Routing System for Garbage Collection

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5th International Conference on Advanced Computing (ICoAC)

作者： Nambiar, Sindhya K. Idicula, Sumam Mary SCMS Sch Engn & Technol Dept Comp Sci Emakulam India Cochin Univ Sci & Technol Dept Comp Sci Emakulam India

ISBN: (纸本)9781479934485

Vehicle Routing Problem existed in the world for decades and there are different variants of the problem depending upon various scenarios. Attempts have been made to arrive at a generic solution applicable to all scenarios with limited success. Moreover, most of the existing solutions deals with static problems where all data are known a priori and the optimal route are arrived using this base data. But in reality, most of the data used to compute the optimal route varies in time and the result need not necessarily be the optimal route. With the advancement of technology, it is possible to capture these time varying data on a real time basis using Agents and can be incorporated to evolve an optimal route as time progress - transforming the static vehicle routing problem into a dynamic vehicle routing problem. In this project, a garbage management system is taken for study. An overall improvement in the performance of the system can be achieved by having an efficient collection and transportation mechanism. Modified Ant Colony Optimization based approach was used to solve the dynamic vehicle routing problem to finally arrive at the optimal routes to visit these collection points and back to the depot.

关键词： Vehicle Routing Problem (VRP) Static VRP (SVRP) Dynamic VRP (DVRP) Clustering dbscan algorithm K-Means algorithm Ant Colony Optimisation

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APPLYING CLUSTERING TECHNIQUES IN HYBRID NETWORK IN THE PRESENCE OF 2D AND 3D OBSTACLES

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COMPUTING AND INFORMATICS 2013年第6期32卷 1170-1191页

作者： Ibrahim, Lamiaa Fattouh Salman, Hesham Abou El Fetouh King Abdulaziz Univ Fac Comp & Informat Technol Jeddah 21551 Saudi Arabia Cairo Univ Inst Stat Studies & Res Cairo Egypt

Clustering spatial data is a well-known problem that has been extensively studied. In the real world, there are many physical obstacles such as rivers, lakes, highways, and mountains, whose presence may substantially affect the clustering result. Although many methods have been proposed in previous works, very few have considered physical obstacles and interlinking bridges. Taking these constraints into account during the clustering process is costly, yet modeling the constraints is paramount for good performance. Owing to saturation in existing telephone networks and the ever increasing demand for wire and wireless services, telecommunication engineers are looking at technologies that can deliver sites and satisfy the demand and level of service constraints in an area with and without obstacles. In this paper, we study the problem of clustering in the presence of obstacles to solve the network planning problem. As such, we modified the Net-Plan algorithm and developed the COD-NETPLAN (Clustering with Obstructed Distance - Network Planning) algorithm to solve the problem of 2D and 3D obstacles. We studied the problem of determining the location of the multi service access node in an area with many mountains and rivers. We used a reachability matrix to detect 2D obstacles, and line segment intersection together with geographical information system techniques for 3D obstacles. Experimental results and the subsequent analysis indicate that the COD-NETPLAN algorithm is both efficient and effective.

关键词： dbscan algorithm clustering algorithm network planning spatial clustering algorithm and obstacles

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Discovery of rules in urban public facility distribution based on dbscan clustering algorithm

Discovery of rules in urban public facility distribution bas...

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Symposium on Remote Sensing and GIS Data Processing and Applications;and Innovative Multispectral Technology and Applications

作者： Li, Xinyan Li, Deren Huazhong Univ Sci & Technol Sch Architecture & Urban Planning 1037 Luoyu Rd Wuhan 430074 Peoples R China Wuhan Univ State Key Lab Info Engn Surveying Mapping & Remot Wuhan 430079 Peoples R China

ISBN: (纸本)9780819469540

Recently Spatial Data Mining (SDM) has been recognized as a powerful technology that can complement traditional GIS to facilitate urban planning and management since it can be used to discover interesting, implicit knowledge from spatial database. dbscan spatial clustering algorithm as a SDM method is able to effectively discover clusters of arbitrary shape in large database with noise points. In this paper we applied this algorithm to detect distribution patterns of urban public facilities in a developed city, including primary school, high school and commercial facilities. Both qualitative and quantitative analysis were carried out to investigate how to determine optimal values of input parameters for dbscan algorithm, and the distribution patterns of public facilities were assessed against urban planning design standard using the algorithm.

关键词： spatial data mining dbscan algorithm GIS urban public facility

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Clustering algorithm Based on Characteristics of Density Distribution

Clustering Algorithm Based on Characteristics of Density Dis...

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2nd IEEE International Conference on Advanced Computer Control

作者： Zheng Hua Wang Zhenxing Zhang Liancheng Wang Qian Natl Digital Switching Syst Engn & Technol R&D Ct Zhengzhou Peoples R China

ISBN: (纸本)9781424458479

Density-based clustering algorithms, which are important algorithms for the task of class identification in spatial database, have many advantages such as no dependence on the number of clusters, ability to discover clusters with arbitrary shapes and handle noise. However, clustering quality of most density-based clustering algorithms degrades when the clusters are of different densities. To address this issue, this paper brings forward a clustering algorithm based on characteristics of density distribution-CCDD algorithm. Firstly, it divides data space into a number of grids. Secondly, it re-divides data space into many smaller partitions, according to each grid's one-dimensional or multi-dimensional characteristics of density distribution. Finally, it uses an improved dbscan algorithm, which chooses different parameters according to each partition's local density, to cluster respectively. The experimental results show that CCDD algorithm, which is superior in quality and efficiency to dbscan algorithm, can find clusters with arbitrary shapes and different densities in spatial databases with noise.

关键词： data mining clustering density-based clustering dbscan algorithm grid data space partition

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