New methods for the initialisation of clusters
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Cited by (62)
The application of spatial domain in optimum initialization for clustering image data using particle swarm optimization
2021, Expert Systems with ApplicationsCitation Excerpt :Their main idea is that if initial centers are selected close to the dense areas of the feature space, then it would be seemed these initial centers are more likely close to the final centroids (Tran, Wehrens, & Buydens, 2003; Yang & Luo, 2005). Based on this concept, various techniques have been proposed, including: dividing the data into subsets and selecting samples with more neighbors(dense points) in each subset (Moh’d B & Roberts, 1996), and selecting samples having maximum frequency in the feature space (Aliwy & Aljanabi, 2017). Density-based methods have a great deal of attention to the distribution and density of the data, but the main disadvantage of these methods is time-consuming, especially for imagery data.
How much can k-means be improved by using better initialization and repeats?
2019, Pattern RecognitionCitation Excerpt :There are three common approaches for this: The first approach divides the space by a regular grid, and counts the frequency of the points in every bucket [76]. The density of a point is then inherited from the bucket it is in.
A k-means based co-clustering (kCC) algorithm for sparse, high dimensional data
2019, Expert Systems with ApplicationsA large scale consensus reaching process managing group hesitation
2018, Knowledge-Based SystemsParallel implementation of Kaufman's initialization for clustering large remote sensing images on clouds
2017, Computers, Environment and Urban SystemsCitation Excerpt :Then, it chooses the next centroid, a point that is farthest from the nearest centroid. AlDaoud and Roberts (1996) proposed a density-based clustering initialization method which partitions the data uniformly into N cells. From each of these cells, a number of centroids are chosen randomly until K centroids are obtained; the number of centroids is proportional to the number of objects in each cell.
A novel approach for initializing the spherical K-means clustering algorithm
2015, Simulation Modelling Practice and TheoryCitation Excerpt :The third family, density estimation methods, includes the Kaufman initialization, which is also described earlier, the KR’s main drawback (as for Kaufman & Rousseeuw in [32]) is the high computational complexity. Another method, by Al-Daoud and Roberts [2], relies on dividing the space Rd into M smaller subspaces, each of which spanned by a proportion of the data points, and the seeds are distributed evenly across these subspaces. For each subspace, initial seeds are chosen randomly, the given method is sensitive to the number of subspaces M, which has to be compatible with k somehow, or else it would affect the density estimation.