In this paper, spatial data correlations are exploited to group sensor nodes into clusters of high data aggregation efficiency. The problem of selecting the set of cluster heads is defined as the weighted connected do...
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In this growing informative world, in every domain, we can get a large amount of raw data, so it is a huge task to find proper and valid information from it. For this task, it is required to categorize data into diffe...
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In seismic interpretation, a big amount of data has to be handled to segment the data cube in zones and faults. In the conventional method, inlines, crosslines and seismic sections are interpreted to divide the geolog...
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ISBN:
(纸本)9789462822177
In seismic interpretation, a big amount of data has to be handled to segment the data cube in zones and faults. In the conventional method, inlines, crosslines and seismic sections are interpreted to divide the geological zones on seismic reflectors and on seismic discontinuities. This segmentation is often guided by seismic attributes, wells and further geological information. The other approach of seismic interpretation is dividing seismic data by algorithms. One popular method to achieve an automatic segmentation is clustering of seismic attributes. There are several clustering algorithms available in all different kinds of scientific disciplines. Some are also already used in seismic interpretation. To get an overview of clustering algorithms and to understand the different kinds of algorithms a research study was done. Therefore, multiple algorithms were classified in a matrix and a workflow was created to test various algorithms on different synthetic 3D seismic data models and subsequently a test environment was founded to understand algorithms to use them for automatic or semiautomatic interpretation of seismic data.
Due to existence of numerous data in huge data sources, organizing and managing those is essential to extract required information according to user preference. Summarization plays a vital role in providing meaningful...
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This paper proposes two new incremental fuzzy c medoids clustering algorithms for very large datasets. These algorithms are tailored to work with continuous data streams, where all the data is not necessarily availabl...
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Energy efficiency is a major concern in Wireless Sensor Networks (WSNs). Many clustering algorithms have been proposed for such a purpose. This paper investigates the existing clustering algorithms. The algorithms hav...
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As one of the important algorithms in data mining, clustering algorithm has a wide range of applications in the real world. However, clustering algorithms have the risk of privacy leakage, such as the k-modes algorith...
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Proposes a methodology that reduces the development of soft learning vector quantization (LVQ) and clustering algorithms to the minimization of an admissible reformulation function using gradient descent. The search f...
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clustering data streams attracted many researchers since the applications that generate data streams have become more popular. Several clustering algorithms have been introduced for data streams based on distance whic...
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clustering groups a set of data object into clusters to maximize the similarity between objects in the same cluster and the dissimilarity between objects in the different clusters. clustering has been applied in many ...
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