Proceedings of the Tenth Workshop on Algorithm Engineering and Experiments and the Fifth Workshop on Analytic Algorithmics and CombinatoricsGoder A, Filkov V (2008) Consensus clustering algorithms: comparison and refinement. In: Proceedings of the workshop on algorithm engineering and experiments, ...
F1 measure was used in order to evaluate precision and quality of clustering that by studying the obtained results, the SOM algorithm obtained high F1 measure. Also a comparison between 2 methods, mapping to two dimensional space and statistical average, performed, that according to the results, ...
A Comparison Study of Cluster Validity Indices Using a Nonhierarchical Clustering Algorithm Cluster analysis is widely used in the initial stages of data analysis and data reduction. The K-means algorithm, a nonhierarchical clustering algorithm, h... Y Shim,J Chung,IC Choi - IEEE 被引量: 40发...
A clustering algorithm is a learning procedure used in computer science that aims to identify the specific characteristics of clusters within a dataset. It is a scheme that provides sensible clusterings by considering only a small fraction of all possible partitions of the data. Clustering algorithms...
Therefore, a consistent hash algorithm was later derived to solve the problem of minimizing data migration and performance when the number of nodes changes. This kind of client-side fragmentation scheme is generally used for relatively stable business data volume, and will not be used in business ...
We propose to explore the well-known clustering algorithm k-means and a recently available one, QuickBundles [1]. We propose an efficient procedure to associate k-means with Point Density Model, a recently proposed metric to analyze geometric structures. We analyze the performance and usability of...
(For K-means we used a "standard" K-means algorithm and a variant of K-means, "bisecting" K-means.) Hierarchical clustering is often portrayed as the better quality clustering approach, but is limited because of its quadratic time complexity. In contrast, K-means and its variants have a ...
ASGC (Axis Shifted Grid Clustering Algorithm轴移动网格聚类) ASGC是一种聚类技术,它结合了基于密度和网格的方法,使用轴移动分割策略(Axis shifted partitioning strategy)对对象进行分组。大部分基于网格的算法的聚类质量受预先设定单元格的大小和单元格密度的影响。该方法使用两个网格结构来减少单元格边界的影响。第二...
Finally, a comparison example was provided, and it was found that the results of the framed algorithm have more rapid convergence, which confirms the practicality and superiority of the developed approach. When employing the proposed clustering algorithm based on q-ROPFLSs, further work is still ...
The ICFS method is evaluated on several datasets by comparison with the original CFS algorithm. Results demonstrate the effectiveness of the proposed method. 展开 关键词: splitting Improved CFS cutoff distance density peaks allocation strategy merging ...