We consider the problem of clustering a finite set of N points in d-dimensional Euclidean space into two clusters minimizing the sum (over both clusters) of the intracluster sums of the squared distances between the cluster elements and their centers. The center of one cluster is defined as a...
Experiments using mitochondrial DNA sequences extracted from several mammals are performed to compare the results of the clustering methods. Results demonstrate the clustering performance and the utility of the proposed algorithms. 展开 关键词: K-means Hierarchical clustering Rank distance Median string ...
Although the K -means algorithm for minimizing the within-cluster sums of squared deviations from cluster centroids is perhaps the most common method for applied cluster analyses, a variety of other criteria are available. The p -median model is an especially well-studied clustering problem that re...
Markov chain and N-Curve are used to study the lane changing pattern and reduction in flow, respectively. Area occupancy has been used as a measure of effectiveness to define the LOS categories for median opening area using K-mean clustering. 展开 ...
Euclidean spaceclustering2-partitionquadratic variationcentercentroidmedianstrong NP-hardnessnonexistence of FPTASapproximation-preserving reductionWe consider the problem of partitioning a set of (N) points in (d) -dimensional Euclidean space into two clusters minimizing the sum of the squared distances ...
Hierarchical clustering heat map, cluster by runs intensity, features by ratio and display log2 ratios to control median
We motivate and develop a new bicriteria measure for assessing the quality of a clustering which avoids the drawbacks of existing measures. A simple recursive heuristic has poly-logarithmic worst-case guarantees under the new measure. Th... R Kannan,S Vempala,A Vetta - 《Journal of the Acm》...