DataClusteringVariedDensityClusteringClusterAnalysisFinding clusters in data is a challenging problem especially when the clusters are being ofwidely varied shapes, sizes, and densities. Herein a new scalable c
proposed a two-phase algorithm for fair $k$-clustering. In the first step, the pointset is partitioned into subsets called fairlets that satisfy the fairness requirement and approximately preserve the $k$-median objective. In the second step, fairlets are merged into $k$ clusters by one of...
The new method we propose is a “dynamic” approach to clustering that simulates communication between connected nodes, then derives a partition from this activity [29]. One type of dynamic clustering is called label propagation [30], in which limited labeled nodes propagate those labels to conne...
(2020). Tad: A trajectory clustering algorithm based on spatial-temporal density analysis. Expert Systems with Applications, 139, 112846. Article Google Scholar Yao, T., Wang, Z., Xie, Z., Gao, J., & Feng, D. D. (2017). Learning universal multiview dictionary for human action ...
This paper proposes a novel method that avoids measuring the prediction accuracy of similar candidates in Euclidean distance space, through an online clustering pruning technique. In addition, our algorithm incorporates a supervised shapelet selection that filters out only those candidates that improve ...
Canopy Clustering ■ Compute distance to centroids and determine the closest centroid for each data point in a Mapper ■ Combine data points in similar clusters ■ Recompute new centroids in reduce task 09.05.2012 DIMA – TU Berlin 34 Map Algorithm 1. map (key, value) Input: centroids, the...
Correlation clusteringLarge scalePseudo-EM algorithmUnsupervised learningWe focus on the problem of correlation clustering, which is to partition data points into clusters so that the repulsion within one cluster and the attraction between clusters could be as small as possible without predefining the ...
The quality of our ensemble merging algorithms was also compared to the quality of clustering all the data at once in memory, the average base clustering (BC) solutions in the ensemble, and to a scalable single pass (SP) algorithm. Access through your organization Check access to the full ...
Clustering trajectory data is an important way to mine hidden information behind moving object sampling data, such as understanding trends in movement patt
T. Zhang, R. Ramakrishnan and M. Livny, "Birch: A New Clustering Algorithm and Its Applications." Data Mining and Knowledge Discovery 1(2). 1997. C. M. Bishop. "Neural Networks for Pattern Recognition".Bayes Theorem. Clarendon Press.Oxford pp. 17-23 (1995). ...