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    Wyświetlanie 1-6 z 6
    Tytuł:
    PaX-DBSCAN: a proposed algorithm for improved clustering
    PaX-DBSCAN: propozycja algorytmu dla doskonalonego grupowania
    Autorzy:
    Samson, Grace L.
    Lu, Joan
    Powiązania:
    https://bibliotekanauki.pl/articles/592926.pdf
    Data publikacji:
    2016
    Wydawca:
    Uniwersytet Ekonomiczny w Katowicach
    Tematy:
    Bulk-loading
    Clustering
    Parallel computing
    Partition
    Spatial database
    Spatial index
    X-tree
    Algorytm bulk loading
    Grupowanie
    Indeks przestrzenny
    Przestrzenne bazy danych
    Przetwarzanie równoległe
    Rozdzielanie
    Struktura drzewiasta X-tree
    Opis:
    We focused on applying parallel computing technique to the bulk loading of X-tree in other to improve the performance of DBSCAN clustering algorithm. We have given a full description of how the system can be archived. We proposed a new parallel algorithm for DBSCAN and another algorithm to extend the X-tree spatial indexing structure. Spatial database systems incorporate space in database systems, they support nontraditional data types and more complex queries, therefore in order to optimise such systems for efficient information processing and retrieval, appropriate techniques must be adopted to facilitate the construction of suitable index structures.
    W artykule autorzy skupiają swoją uwagę na zastosowaniu techniki przetwarzania równoległego przy wykorzystaniu struktur drzewiastych X-tree i algorytmu bulk loading. Zaproponowano nowy algorytm przetwarzania równoległego DBSCAN i drugi algorytm dla rozszerzania struktur indeksowania przestrzennego. Algorytm grupowania DBSCAN jest efektywnym algorytmem grupowania dla Systemów Przestrzennych Baz Danych, który ma możliwość wykrywania zakłóceń i nie wymaga znacznej liczby skupień wcześniej ustalonych, jednakże działanie algorytmu zmienia się, gdy rozmiar danych jest duży. Ten algorytm może nie działać optymalnie, jeśli niewłaściwe wartości są wybrane dla minpts i eps. Dlatego nowy zaproponowany algorytm powinien eliminować te ograniczenia.
    Źródło:
    Studia Ekonomiczne; 2016, 296; 86-121
    2083-8611
    Pojawia się w:
    Studia Ekonomiczne
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Wild Image Retrieval with HAAR Features and Hybrid DBSCAN Clustering For 3D Cultural Artefact Landmarks Reconstruction
    Autorzy:
    Pitchandi, Perumal
    Powiązania:
    https://bibliotekanauki.pl/articles/2201730.pdf
    Data publikacji:
    2022
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    outliers removal
    culturalartefact objects
    3D reconstruction
    particle swarm optimization
    PSO
    spatial clustering
    density based spatial clustering
    noise clustering algorithm
    Opis:
    In this digital age large amounts of information, images and videos can be found in the web repositories which accumulate this information. These repositories include personal, historic, cultural, and business event images. Image mining is a limited field in research where most techniques look at processing images instead of mining. Very limited tools are found for mining these images, specifically 3D (Three Dimensional) images. Open source image datasets are not structured making it difficult for query based retrievals. Techniques extracting visual features from these datasets result in low precision values as images lack proper descriptions or numerous samples exist for the same image or images are in 3D. This work proposes an extraction scheme for retrieving cultural artefact based on voxel descriptors. Image anomalies are eliminated with a new clustering technique and the 3D images are used for reconstructing cultural artefact objects. Corresponding cultural 3D images are grouped for a 3D reconstruction engine’s optimized performance. Spatial clustering techniques based on density like PVDBSCAN (Particle Varied Density Based Spatial Clustering of Applications with Noise) eliminate image outliers. Hence, PVDBSCAN is selected in this work for its capability to handle a variety of outliers. Clustering based on Information theory is also used in this work to identify cultural object’s image views which are then reconstructed using 3D motions. The proposed scheme is benchmarked with DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to prove the proposed scheme’s efficiency. Evaluation on a dataset of about 31,000 cultural heritage images being retrieved from internet collections with many outliers indicate the robustness and cost effectiveness of the proposed method towards a reliable and just-in-time 3D reconstruction than existing state-of-the-art techniques.
    Źródło:
    Advances in Science and Technology. Research Journal; 2022, 16, 3; 269--281
    2299-8624
    Pojawia się w:
    Advances in Science and Technology. Research Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Analytics and data science applied to the trajectory outlier detection
    Autorzy:
    Lopez, Alexis J.
    Quintero, Perfecto M.
    Hernandez, Ana K.
    Powiązania:
    https://bibliotekanauki.pl/articles/117731.pdf
    Data publikacji:
    2020
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    spatial-temporal data
    trajectory outlier detection
    trajectory clustering
    dane przestrzenno-czasowe
    wykrywanie wartości odstających trajektorii
    grupowanie trajektorii
    Opis:
    Nowadays, logistics for transportation and distribution of merchandise are a key element to increase the competitiveness of companies. However, the election of alternative routes outside the panned routes causes the logistic companies to provide a poor-quality service, with units that endanger the appropriate deliver of merchandise and impacting negatively the way in which the supply chain works. This paper aims to develop a module that allows the processing, analysis and deployment of satellite information oriented to the pattern analysis, to find anomalies in the paths of the operators by implementing the algorithm TODS, to be able to help in the decision making. The experimental results show that the algorithm detects optimally the abnormal routes using historical data as a base.
    Źródło:
    Applied Computer Science; 2020, 16, 2; 5-17
    1895-3735
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Analysis of the Crime rate in Poland in Spatial and Temporal Terms
    Przestrzenno – czasowa analiza zjawiska przestępczości w Polsce
    Autorzy:
    Kądziołka, Kinga
    Powiązania:
    https://bibliotekanauki.pl/articles/529741.pdf
    Data publikacji:
    2016
    Wydawca:
    Wyższa Szkoła Bankowa we Wrocławiu
    Tematy:
    crime rate
    structure of crime
    determinants of crime
    spatial lag model
    Moran statistic
    variable clustering
    Opis:
    The paper evaluates the crime rate in Poland in spatial and temporal terms. The methods of spatial statistics were employed to identify the clusters of areas with above-average intensity of the selected categories of crimes. Poviats were divided into four groups according to their location in the quadrants of Moran scatter plot. The spatial lag model was used to identify certain spatial relationships between general crime rate and the selected factors recognised in the literature as factors that affect crime. The initial set of potential independent variables was selected arbitrarily. Then Ward's method was used to reduce the number of correlated variables. The following factors were found to significantly explain spatial variation of crime rate in the poviats of Poland: the intensity of crime in the surrounding areas, urbanisation, percentage of single-person households, divorce’s coefficient, gross migration per 1000 population and provided accommodation per 1000 population. The analysis also involved the structure and dynamics of crimes recorded in Poland. It was pointed out that the changes in law, the development of information technology and the increase the level of education significantly affected the number and structure of the crimes recorded in police statistics.
    Źródło:
    Central and Eastern European Journal of Management and Economics (CEEJME); 2016, 1; 81-96
    2353-9119
    Pojawia się w:
    Central and Eastern European Journal of Management and Economics (CEEJME)
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Analysis of socio-economic spatial structure of urban agglomeration in China based on spatial gradient and clustering
    Autorzy:
    He, Li
    Tao, Jian’ge
    Meng, Ping
    Chen, Dan
    Yan, Meng
    Vasa, László
    Powiązania:
    https://bibliotekanauki.pl/articles/19233717.pdf
    Data publikacji:
    2021
    Wydawca:
    Instytut Badań Gospodarczych
    Tematy:
    economic and social development
    urban agglomeration
    Central Plains Urban Agglomeration (CPUA)
    clustering
    spatial gradient
    Opis:
    Research background: Previous studies on the economic and social development of urban agglomerations mostly focus on a single primacy comparative analysis and efficiency evaluation. Spatial structure differentiation is an important feature of urban agglomeration. The lack of economic and social analysis on the spatial structure makes it impossible to determine the development positioning of each city in the urban agglomeration, which affects the sustainable economic development ability of these areas. Purpose of the article: The objective of the article is to analyze the spatial development law and experience of urban agglomeration, this study explores the practice of economic and population spatial structure of city areas in China. For this purpose, CPUA and its central city Zhengzhou was taken as an example, the spatial gradient structure of example was analyzed. Methods: Using economic and population data of 32 cities in this region, growth pole theory, and pole-axis theory, the economic and population spatial structure of urban agglomeration, the spatial gradient structure of central cities in urban agglomerations were analyzed with the method of cluster about radiation index. Findings & value added: (1) In the process of the formation of CPUA, the geo-graphical spatial pattern plays a decisive role in economic and social development. This is an experience from developing countries. (2) CPUA presents a gradient development pattern with Zhengzhou as the center, and economic and social development gradually radiates to the metropolitan area, the core development area, and the character development demonstration area. (3) The economic and social gradients of Zhengzhou, the central city, present the hierarchy rules and characteristics which are driven by the Beijing-Guangzhou-Railway axis and the Longhai-Railway axis. (4) The central city of Zhengzhou still presents insufficient primacy in regional development, which shows that Zhengzhou accounts for 6% of the population of the Central Plains Economic Zone and 14% of GDP, and insufficient agglomeration. Different countries at different stages of economic development have different urban agglomeration development models. The conclusions from China provide new decision-making ideas and methods for spatial structure research and development strategy analysis of urban agglomerations.
    Źródło:
    Oeconomia Copernicana; 2021, 12, 3; 789-819
    2083-1277
    Pojawia się w:
    Oeconomia Copernicana
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Fast FCM with spatial neighborhood information for brain MR image segmentation
    Autorzy:
    Biniaz, A.
    Abbasi, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/91616.pdf
    Data publikacji:
    2013
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    Fuzzy c-Means clustering
    FCM
    Fast FCM
    FFCM
    spatial Fast FCM
    sFFCM
    MR image
    noise interference
    Opis:
    Among different segmentation approaches Fuzzy c-Means clustering (FCM) is a welldeveloped algorithm for medical image segmentation. In emergency medical applications quick convergence of FCM is necessary. On the other hand spatial information is seldom exploited in standard FCM; therefore nuisance factors can simply affect it and cause misclassification. This paper aims to introduce a Fast FCM (FFCM) technique by incorporation of spatial neighborhood information which is exploited by a linear function on fuzzy membership. Applying proposed spatial Fast FCM (sFFCM), elapsed time is decreased and neighborhood spatial information is exploited in FFCM. Moreover, iteration numbers by proposed FFCM/sFFCM techniques are decreased efficiently. The FCM/FFCM techniques are examined on both simulated and real MR images. Furthermore, to considerably decrease of convergence time and iterations number, cluster centroids are initialized by an algorithm. Accuracy of the new approach is same as standard FCM. The quantitative assessments of presented FCM/FFCM techniques are evaluated by conventional validity functions. Experimental results demonstrate that sFFCM techniques efficiently handle noise interference and significantly decrease elapsed time.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2013, 3, 1; 15-25
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-6 z 6

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