Spatial indexing is an important research in the field of spatial databases, and plays a key role in how to efficiently perform spatial data retrieval and query. In this paper, a new hierarchical clustering algorithm ...
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Due to resource constraints in Wireless Sensor Networks (WSNs), this paper contributes a distributed clustering algorithm suitable for a large scale Voronoi cell-based WSNs with sensors randomly deployed according to ...
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Consensus clustering is a stability-based algorithm with a prediction power far better than other internal measures. Unfortunately, this method is reported to be slow in terms of time and hard to scalability. We prese...
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The problem of clustering data has been driven by a demand from various disciplines engaged in exploratory data analysis, such as medicine taxonomy, customer relationship management and so on. However, Most of the alg...
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Circular data, i.e., data in the form of 'natural' directions or angles are very common in a number of different areas such as biological, meteorological, geological, and political sciences. clustering circula...
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Circular data, i.e., data in the form of 'natural' directions or angles are very common in a number of different areas such as biological, meteorological, geological, and political sciences. clustering circular data is not an easy task due to the circular geometry of the data space. Some clustering approaches, such as the spherical k-means, use the cosine distance instead of the euclidean distance in order to measure the difference between points. In this paper, we propose a variation of the randomized gravitational clustering algorithm in order to deal with circular data. Basically, we use the cosine distance, we modify the gravitational law in order to use the cosine distance and we use geodesics ('straight' lines in curved spaces) in order to move points according to the gravitational dynamic. Our initial experiments indicate that the spherical gravitational clustering algorithm is able to find clusters in noisy circular data.
This paper presents two algorithms, one for clustering a set of interconnected nodes and the other for forming a linear placement of clustered interconnected nodes. The linear placement algorithm requires the output o...
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ISBN:
(纸本)0897912675
This paper presents two algorithms, one for clustering a set of interconnected nodes and the other for forming a linear placement of clustered interconnected nodes. The linear placement algorithm requires the output of clustering as an input. The two algorithms were designed to analyze the structure of digital logic for automatic placement of logic functions on a MOS/LSI chip as part of an automatic layoui, system8 and so far have only been used for that application. However, the clustering algorithm could be used to analyze any undirected graph. Both algorithms are noniterative and provide very good results with small amounts of computer time.
This paper presents a new clustering algorithm inspired by Newtonian gravity that iteratively groups data and eliminates outliers. In particular, we impose a grid over the region of interest and define a particle with...
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The continuous development of modern technology has gradually changed the way computers transmit information. Network information is in a world of mobile information transmission. While large data brings convenience t...
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clustering is a primitive and important operator that analyzes a given dataset to discover its hidden patterns and features. Because datasets are usually updated dynamically (i.e., it accepts continuous insertions and...
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Trajectory clustering is attractive for the task of class identification in spatial database. Existing trajectory clustering algorithm TRCLUS uses global parameters to discover common trajectories. However, it can not...
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