Читать книгу Bioinformatics and Medical Applications - Группа авторов - Страница 24

K means, an unsupervised algorithm, endeavors to iteratively segment the dataset into K pre-characterized and nonoverlapping data groups with the end goal that one data point can have a place with just one bunch. It attempts to make the intra-group data as similar as could reasonably be expected while keeping the bunches as various (far) as could be expected under the circumstances. It appoints data points to a cluster with the end goal that the entirety of the squared separation between the data points and the group’s centroid is at the minimum. The less variety we have inside bunches, the more homogeneous the data points are inside a similar group.