Results 241 to 250 of about 232,386 (277)
Some of the next articles are maybe not open access.
Spectral Cluster Maps Versus Spectral Clustering
2020The paper investigates several notions of graph Laplacians and graph kernels from the perspective of understanding the graph clustering via the graph embedding into an Euclidean space. We propose hereby a unified view of spectral graph clustering and kernel clustering methods.
Sławomir T. Wierzchoń +1 more
openaire +1 more source
Spectral sparsification in spectral clustering
2016 23rd International Conference on Pattern Recognition (ICPR), 2016Graph spectral clustering algorithms have been shown to be effective in finding clusters and generally outperform traditional clustering algorithms, such as k-means. However, they have scalibility issues in both memory usage and computational time. To overcome these limitations, the common approaches sparsify the similarity matrix by zeroing out some ...
Alireza Chakeri +2 more
openaire +1 more source
2016 IEEE 32nd International Conference on Data Engineering (ICDE), 2016
Clustering is a classical data mining task used for discovering interrelated pattern of similarities in the data. In many modern day domains, data is getting continuously generated as a stream. For scalability reasons, clustering the points in a data stream requires designing single pass, limited memory streaming clustering algorithms.
Shinjae Yoo +2 more
openaire +1 more source
Clustering is a classical data mining task used for discovering interrelated pattern of similarities in the data. In many modern day domains, data is getting continuously generated as a stream. For scalability reasons, clustering the points in a data stream requires designing single pass, limited memory streaming clustering algorithms.
Shinjae Yoo +2 more
openaire +1 more source
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015
Ensemble clustering, also known as consensus clustering, is emerging as a promising solution for multi-source and/or heterogeneous data clustering. The co-association matrix based method, which redefines the ensemble clustering problem as a classical graph partition problem, is a landmark method in this area.
Hongfu Liu +4 more
openaire +1 more source
Ensemble clustering, also known as consensus clustering, is emerging as a promising solution for multi-source and/or heterogeneous data clustering. The co-association matrix based method, which redefines the ensemble clustering problem as a classical graph partition problem, is a landmark method in this area.
Hongfu Liu +4 more
openaire +1 more source
Compressed Spectral Clustering
2009 IEEE International Conference on Data Mining Workshops, 2009Compressed sensing has received much attention in both data mining and signal processing communities. In this paper, we provide theoretical results to show that compressed spectral clustering, separating data samples into different clusters directly in the compressed measurement domain, is possible.
Bin Zhao, Changshui Zhang
openaire +1 more source
Compressive Spectral Clustering
AIP Conference Proceedings, 2010Data mining has become one of the fastest growing research topics in mathematics and computer science. Data such as high dimensional signals, magnetic resonance images, and hyperspectral images can be costly to acquire or it could be unobtainable to make even simple direct comparisons. Compressed sensing is a technique that addresses this issue.
Blake Hunter +4 more
openaire +1 more source
Incremental Spectral Clustering
2012In the present contribution, a novel algorithm for off-line spectral clustering algorithm is introduced and an online extension is derived in order to deal with sequential data. The proposed algorithm aims at dealing with nonconvex clusters having different forms.
Abdelhamid Bouchachia, Markus Prossegger
openaire +1 more source
Approximate Spectral Clustering
2009While spectral clustering has recently shown great promise, computational cost makes it infeasible for use with large data sets. To address this computational challenge, this paper considers the problem of approximate spectral clustering, which enables both the feasibility (of approximately clustering in very large and unloadable data sets) and ...
Liang Wang +3 more
openaire +1 more source
2008
In this paper, we propose a novel spectral clustering algorithm called: Locality Spectral Clustering ( Lsc ) which assumes that each data point can be linearly reconstructed from its local neighborhoods. The Lsc algorithm firstly try to learn a smooth enough manifold structure on the data manifold and then computes the eigenvectors on the smooth ...
Yun-Chao Gong, Chuanliang Chen
openaire +1 more source
In this paper, we propose a novel spectral clustering algorithm called: Locality Spectral Clustering ( Lsc ) which assumes that each data point can be linearly reconstructed from its local neighborhoods. The Lsc algorithm firstly try to learn a smooth enough manifold structure on the data manifold and then computes the eigenvectors on the smooth ...
Yun-Chao Gong, Chuanliang Chen
openaire +1 more source