Spectral Clustering of CRISM Datasets in Jezero Crater Using UMAP and k-Means

Spectral Rotation versus K-Means in Spectral Clustering

Spectral clustering has been a popular data clustering algorithm. This category of approaches often resort to other clustering methods, such as K-Means, to get the final cluster. The potential flaw of such common practice is that the obtained relaxed continuous spectral solution could severely deviate from the true discrete solution. In this paper, we propose to impose an additional orthonormal...

Spectral Relaxation for K-means Clustering

The popular K-means clustering partitions a data set by minimizing a sum-of-squares cost function. A coordinate descend method is then used to find local minima. In this paper we show that the minimization can be reformulated as a trace maximization problem associated with the Gram matrix of the data vectors. Furthermore, we show that a relaxed version of the trace maximization problem possesse...

Extending Crism Spectral Coverage in Gale Crater Using Themis-vis and Hirise

Canonical PSO Based K-Means Clustering Approach for Real Datasets

"Clustering" the significance and application of this technique is spread over various fields. Clustering is an unsupervised process in data mining, that is why the proper evaluation of the results and measuring the compactness and separability of the clusters are important issues. The procedure of evaluating the results of a clustering algorithm is known as cluster validity measure. Different ...

Comparative Study of K-Means, Pam and Rough K-Means Algorithms Using Cancer Datasets

Data mining is a search for relationship and patterns that exist in large database. Clustering is an important data mining technique. Because of the complexity and the high dimensionality of gene expression data, classification of a disease samples remains a challenge. Hierarchical clustering and partitioning clustering is used to identify patterns of gene expression useful for classification o...