官术网_书友最值得收藏!

  • Machine Learning in Java
  • AshishSingh Bhatia Bostjan Kaluza
  • 217字
  • 2021-06-10 19:29:59

Clustering

Clustering is a technique for grouping similar instances into clusters according to some distance measures. The main idea is to put instances that are similar (that is, close to each other) into the same cluster, while keeping the dissimilar points (that is, the ones further apart from each other) in different clusters. An example of how clusters might look like is shown in the following diagram:

The clustering algorithms follow two fundamentally different approaches. The first is a hierarchical or agglomerative approach that first considers each point as its own cluster, and then iteratively merges the most similar clusters together. It stops when further merging reaches a predefined number of clusters, or if the clusters to be merged are spread over a large region.

The other approach is based on point assignment. First, initial cluster centers (that is, centroids) are estimated, for instance, randomly, and then, each point is assigned to the closest cluster, until all of the points are assigned. The most well known algorithm in this group is k-means clustering.

The k-means clustering either picks initial cluster centers as points that are as far as possible from one another, or (hierarchically) clusters a sample of data and picks a point that is the closest to the center of each of the k-clusters.

主站蜘蛛池模板: 洮南市| 浙江省| 桂阳县| 阳泉市| 内乡县| 乌恰县| 玉门市| 和平县| 浦城县| 鄱阳县| 汝阳县| 锡林浩特市| 全州县| 江西省| 会理县| 贞丰县| 辽宁省| 察雅县| 宣化县| 老河口市| 湛江市| 德化县| 雷山县| 措勤县| 翁源县| 荣昌县| 宁津县| 修武县| 绵竹市| 云林县| 威远县| 大姚县| 奈曼旗| 海伦市| 蕉岭县| 冀州市| 山阴县| 儋州市| 香河县| 都兰县| 长宁县|