Clustering has been widely used to identify possible structures in data and help users understand data in an unsupervised manner.

... global and local structure of the original input data. It is important to note that this objective function uses a ... subspace clustering. Two cost functions named LLE-SSC and LLE-LRR are proposed [39] with the aid of LLE ...

... local minimum can be an issue Laplacian Score ( LS ) ( 4 ) Minimum Redundancy Maximum Relevance ( mRMR ) ( 5 ) Correlation - based Feature Selection ( CFS ) ( 6 ) Sparse Subspace Clustering ... preserving the local structure of data • Can ...

... local non-negative pursuit method for intrinsic manifold structure preservation. In: Proceedings of AAAI Conference ... subspace clustering: algorithm, theory, and applications. IEEE Trans. Pattern Anal. Mach. Intell. 35(11), 2765 ...

... clustering via non-negative matrix factorization tailored graph tensor over distributed networks. IEEE J. Select. Areas Commun. (2020) 12. Ren, Z., Sun, Q.: Simultaneous global and local graph structure preserving for multiple kernel ...

Data mining focuses on finding previously unknown yet potentially useful, hidden patterns from large amounts of data.

24th International Conference, ICONIP 2017, Guangzhou, China, November 14–18, 2017, Proceedings, Part VI Derong Liu, Shengli Xie, Yuanqing Li, Dongbin Zhao, El-Sayed M. El-Alfy. Subspace Clustering via Adaptive Low - Rank Model Mingbo ...

Each point may represent simply locationor, abstractly, any entity expressible as a vector in finite-dimensional Euclidean space.The answer to the question posed is that very much can be known about the points;the mathematics of this ...

The first part of the book presents applications of spectral methods to problems from a variety of topics including combinatorial optimization, learning and clustering. The second part of the book is motivated by efficiency considerations.