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: 长汀县| 石渠县| 平安县| 白银市| 贵定县| 宝兴县| 海安县| 闽侯县| 星子县| 白银市| 商洛市| 游戏| 五峰| 曲靖市| 福清市| 棋牌| 昭苏县| 尉氏县| 京山县| 湘阴县| 乌拉特后旗| 阿拉善盟| 酉阳| 南丰县| 中卫市| 奉贤区| 大化| 砀山县| 巴南区| 根河市| 丽水市| 松潘县| 晋宁县| 信宜市| 台中市| 巴青县| 得荣县| 安徽省| 达日县| 来凤县| 天峨县|