Clustering Kanker Serviks Berdasarkan Perbandingan Euclidean dan Manhattan Menggunakan Metode K-Means

K-means a fairly simple and commonly used cluster of clusters to partition datasets into multiple clusters. Distance calculations are used to find similar data objects that lead to developing powerful algorithms for datamining such as classification and grouping. Some studies apply k-means algorithms using distance calculations such as Euclidean, Manhattan and Minkowski. The study used datasets from gynecological patients with a total of 401 patients examined and as many as 205 patients detected cervical cancer, while 196 other patients did not have cervical cancer. The results were shown with the help of confusion matrix and ROC curve, accuracy value obtained by 79.30% with ROC 79.17% on K-Means Euclidean Metric while K-Means Manhattan Metric by 67.83% with ROC 65.94%. Thus it can be concluded that the Euclidean method is the best method to be applied in the K-Means Clustering algorithm on cervical cancer datasets.

Multiple kernel learning for integrative consensus clustering of omic datasets

Motivation Diverse applications—particularly in tumour subtyping—have demonstrated the importance of integrative clustering techniques for combining information from multiple data sources. Cluster Of Clusters Analysis (COCA) is one such approach that has been widely applied in the context of tumour subtyping. However, the properties of COCA have never been systematically explored, and its robustness to the inclusion of noisy datasets is unclear. Results We rigorously benchmark COCA, and present Kernel Learning Integrative Clustering (KLIC) as an alternative strategy. KLIC frames the challenge of combining clustering structures as a multiple kernel learning problem, in which different datasets each provide a weighted contribution to the final clustering. This allows the contribution of noisy datasets to be down-weighted relative to more informative datasets. We compare the performances of KLIC and COCA in a variety of situations through simulation studies. We also present the output of KLIC and COCA in real data applications to cancer subtyping and transcriptional module discovery. Availability and implementation R packages klic and coca are available on the Comprehensive R Archive Network. Supplementary information Supplementary data are available at Bioinformatics online.