Semiconductor Bonding Equipment Grouping Model Based on Processing Task Matching
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Adjacency matrix
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Graph algorithms that test adjacencies are usually implemented with an adjacency-matrix representation because the adjacency test takes constant time with adjacency matrices, but it takes linear time in the degree of the vertices with adjacency lists. In this article, we review the adjacency-map representation, which supports adjacency tests in constant expected time, and we show that graph algorithms run faster with adjacency maps than with adjacency lists by a small constant factor if they do not test adjacencies and by one or two orders of magnitude if they perform adjacency tests.
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Abstract Commonly useful properties of directed graphs and their adjacency matrices are briefly reviewed. Some misconceptions presented in earlier papers, relating to indicators in the adjacency matrix of the occurrence of multiple laps of cycles and of compound cycles composed of several shorter cycles, are corrected. Shortcomings of an earlier method for finding cycles are discussed and a simple, improved method for finding all cycles, or all paths between two nodes of interest, is presented. Shortcomings of an earlier method of suppressing unwanted cycle effects are discussed and a simple, improved method for this is presented. Results obtained from this method are compared with those for the same case where cycle suppression is not used. KEY WORDS: Graphsnetworkspathscycles
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The shortcomings about these days clustering algorithm considering the condition of planar adjacency relationship are analysised.The clustering algorithm considering the condition of planar adjacency relationship is defined again newly from the general clustering.In order to dealing with the clustering considering the condition of planar adjacency relationship,the concept adjacency matrix is defined.The genetic-clustering algorithm considering the condition of planar adjacency relationship is put forward,partitioning samples on the best-close distance and adjacency matrix,calculating cluster aim function on within-group sum of squares(WGSS) error,importing genetic algorithm.The algorithm is validated and compared with the FCM clustering outcome by examples.Algorithm testing show: the genetic-clustering algorithm considering the condition of planar adjacency relationship is completely feasible and availability.
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We present a novel technique-Compressed Adjacency Matrices-for visualizing gene regulatory networks. These directed networks have strong structural characteristics: out-degrees with a scale-free distribution, in-degrees bound by a low maximum, and few and small cycles. Standard visualization techniques, such as node-link diagrams and adjacency matrices, are impeded by these network characteristics. The scale-free distribution of out-degrees causes a high number of intersecting edges in node-link diagrams. Adjacency matrices become space-inefficient due to the low in-degrees and the resulting sparse network. Compressed adjacency matrices, however, exploit these structural characteristics. By cutting open and rearranging an adjacency matrix, we achieve a compact and neatly-arranged visualization. Compressed adjacency matrices allow for easy detection of subnetworks with a specific structure, so-called motifs, which provide important knowledge about gene regulatory networks to domain experts. We summarize motifs commonly referred to in the literature, and relate them to network analysis tasks common to the visualization domain. We show that a user can easily find the important motifs in compressed adjacency matrices, and that this is hard in standard adjacency matrix and node-link diagrams. We also demonstrate that interaction techniques for standard adjacency matrices can be used for our compressed variant. These techniques include rearrangement clustering, highlighting, and filtering.
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The three most common graph representations, namely the adjacency matrix, one way adjacency lists and adjacency multilist, are implemented in PASCAL and their performance evaluated for twelve graphs typical to computer network configurations.
We show that both adjacency multilist and one way adjacency lists are preferred over the adjacency matrix representation. Although their implementation is Slightly more complicated it out performs the latter by a factor of at least 5.
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