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A Crossover-Imaged Clustering Algorithm with Bottom-up Tree Architecture
http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/73012
title: A Crossover-Imaged Clustering Algorithm with Bottom-up Tree ArchitectureAn Adaptable Deflect and Conquer Clustering Algorithm
http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/37319
title: An Adaptable Deflect and Conquer Clustering Algorithm abstract: The grid-based clustering algorithm is an efficient clustering algorithm, but the effect of the algorithm is seriously influenced by the size of the predefined grids and the threshold of the significant cells. Thus, in this paper, to reduce the influences of the size of the predefined grids and the threshold of the significant cells, we adopt deflect and conquer techniques to propose a new grid-based clustering algorithm, which is called Adaptable Deflect and Conquer Clustering (ADCC) algorithm. The idea of ADCC is to utilize the predefined grids and predefined threshold to identify the significant cells, by which nearby cells that are also significant can be merged to develop a cluster in the first place. Next, the modified grids which are deflected to half size of the grid are used to identify the significant cells again. Finally, the new generated significant cells and the initial significant cells are merged so as to offset the round-off error and improve the precision of clustering task. And we verify by experiment that the performance of our new grid-based clustering algorithm, ADCC, is good.
<br>最大序列型樣的的快速探勘
http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/18163
title: 最大序列型樣的的快速探勘Mining Negative Fuzzy Sequential Patterns
http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/18162
title: Mining Negative Fuzzy Sequential PatternsAn Adaptive Crossover-Imaged Clustering Algorithm
http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/18161
title: An Adaptive Crossover-Imaged Clustering Algorithm abstract: The grid-based clustering algorithm is an efficient clustering algorithm, but its effect is seriously influenced by the size of the predefined grids and the threshold of the significant cells. The data space will be partitioned into a finite number of cells to form a grid structure and then performs all clustering operations on this obtained grid structure. To cluster efficiently and simultaneously, to reduce the influences of the size of the cells and inherits the advantage with the low time complexity, an Adaptive Crossover-Imaged Clustering Algorithm, called ACICA, is proposed in this paper. The main idea of ACICA algorithm is to deflect the original grid structure in each dimension of the data space after the image of significant cells generated from the original grid structure have been obtained. Because the deflected grid structure can be considered a dynamic adjustment of the size of original cells and the threshold of significant cells, the new image generated from this deflected grid structure will be used to revise the originally obtained significant cells. Hence, the new image of significant cells is projected on the original grid structure to be the crossover image. Finally the clusters will be generated from this crossover image. The experimental results verify that, indeed, the effect of ACICA algorithm is less influenced by the size of the cells than other grid-based algorithms. Finally, we will verify by experiment that the results of our proposed ACICA algorithm outperforms than others.
<br>A Crossover-Imaged Clustering Algorithm with Bottom-Up Tree Architecture
http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/18160
title: A Crossover-Imaged Clustering Algorithm with Bottom-Up Tree Architecture abstract: The grid-based clustering algorithms are efficient with low computation time, but the size of the predefined grids and the threshold of the significant cells are seriously influenced their effects. The ADCC [1] and ACICA+ [2] are two new grid-based clustering algorithms. The ADCC algorithm uses axis-shifted strategy and cell clustering twice to reduce the influences of the size of the cells and inherits the advantage with the low time complexity. And the ACICA+ uses the crossover image of significant cells and just only one cell clustering. But the extension of original significant cell in one crossover image is not easy to find what else clusters it belongs to. The crossover-imaged clustering algorithm with bottom-up tree architecture, called CIC-BTA, is proposed to use bottom-up tree architecture to have the same results. The main idea of CIC-BTA algorithm is to use the bottom-up tree architecture to link the significant cells to be the pre-clusters and combine pre-clusters into one by using semi-significant cells The final set of clusters is the result.
<br>以 FAT 演算法挖掘頻繁學習序列
http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/18159
title: 以 FAT 演算法挖掘頻繁學習序列