To systematically explore the research hotspots and frontier trends in the field of data mining then domestic county medical community, and to provide data reference for future in-depth research in this field. Co-occu...
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The minimum spanning tree clustering algorithm is known to be capable of detecting clusters with irregular boundaries. In this paper, we propose two minimum spanning tree based clustering algorithms. The first algorit...
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In this paper, we give a short review of recent developments in clustering. We shortly summarize important clustering paradigms before addressing important topics including metric adaptation in clustering, dealing wit...
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Traffic Road accidents have always been a significant cause of death in the Country. Many solutions have been developed over the years to reduce the problem of traffic and road accidents, but not much success has been...
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clustering high-dimensional data spaces are often encountered in areas such as medicine, DNA analysis in computational biology and many others. It imposes on a data analysis severe computational requirements and prese...
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ISBN:
(纸本)9781450332163
clustering high-dimensional data spaces are often encountered in areas such as medicine, DNA analysis in computational biology and many others. It imposes on a data analysis severe computational requirements and present real challenges to clustering algorithms. This paper evaluates the performance efficiency of K-means and Agglomerative hierarchical clustering methods based on Euclidean and Manhattan distance functions for high dimensional data. Efficiency concerns the computational time required to build up datasets. Extensive experiments carried out to evaluate two clustering methods on Microarray datasets. The results demonstrate that Agglomerative hierarchical clustering algorithm is efficient in time for both distance functions than K-means clustering algorithm.
An improved artificial fish swarm algorithm (IAFSA) is proposed, and its complexity is much less than the original algorithm (AFSA) because of a new proposed fish behavior. Based on IAFSA, two novel algorithms for dat...
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Software size and complexity in all kinds of domains increase rapidly in recent times, making software maintenance and reusability increasingly difficult. Analysis of existing software and subsequent re-modularization...
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In competitive electricity markets, the distribution service providers have been given new degrees of freedom in formulating dedicated tariff offers to be applied to properly defined customer classes. For this purpose...
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Matrix clustering algorithms are among the oldest approaches to the vertical partitioning problem. They can be summarized as follows: (1) given a workload, construct an Attribute Usage Matrix (AUM), (2) apply some kin...
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Matrix clustering algorithms are among the oldest approaches to the vertical partitioning problem. They can be summarized as follows: (1) given a workload, construct an Attribute Usage Matrix (AUM), (2) apply some kind of a row and column permutation algorithm and (3) extract the resulting clusters which define the required fragments. This naive approach holds some promise for a number of contemporary applications: (1) dynamization of vertical partitioning (2) big data applications and other cases of resource constraints (3) tuning of multistores. In this paper we examine a number of existing matrix clustering algorithms used for vertical partitioning. We study these algorithms and assess the quality of the solutions. The experiments are run on the TPC-H workload using the PostgreSQL DBMS.
In this paper, we investigate the problem of quality analysis of clustering results using semantic annotations given by experts. In previous work we proposed a novel approach to construction of evaluation measure, cal...
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