- The Unsupervised Learning Workshop
- Aaron Jones Christopher Kruger Benjamin Johnston
- 336字
- 2021-06-18 18:12:52
Summary
In this chapter, we discussed how hierarchical clustering works and where it may be best employed. In particular, we discussed various aspects of how clusters can be subjectively chosen through the evaluation of a dendrogram plot. This is a huge advantage over k-means clustering if you have absolutely no idea of what you're looking for in the data. Two key parameters that drive the success of hierarchical clustering were also discussed: the agglomerative versus pisive approach and linkage criteria. Agglomerative clustering takes a bottom-up approach by recursively grouping nearby data together until it results in one large cluster. Divisive clustering takes a top-down approach by starting with the one large cluster and recursively breaking it down until each data point falls into its own cluster. Divisive clustering has the potential to be more accurate since it has a complete view of the data from the start; however, it adds a layer of complexity that can decrease the stability and increase the runtime.
Linkage criteria grapples with the concept of how distance is calculated between candidate clusters. We have explored how centroids can make an appearance again beyond k-means clustering, as well as single and complete linkage criteria. Single linkage finds cluster distances by comparing the closest points in each cluster, while complete linkage finds cluster distances by comparing more distant points in each cluster. With the knowledge that you have gained in this chapter, you are now able to evaluate how both k-means and hierarchical clustering can best fit the challenge that you are working on.
While hierarchical clustering can result in better performance than k-means due to its increased complexity, please remember that more complexity is not always good. Your duty as a practitioner of unsupervised learning is to explore all the options and identify the solution that is both resource-efficient and performant. In the next chapter, we will cover a clustering approach that will serve us best when it comes to highly complex and noisy data: Density-Based Spatial Clustering of Applications with Noise.
- Learning Cocos2d-x Game Development
- Instant uTorrent
- 網絡服務器配置與管理(第3版)
- 計算機組裝與系統配置
- 精選單片機設計與制作30例(第2版)
- AMD FPGA設計優化寶典:面向Vivado/SystemVerilog
- 微軟互聯網信息服務(IIS)最佳實踐 (微軟技術開發者叢書)
- 計算機組裝與維護(第3版)
- Hands-On Artificial Intelligence for Banking
- 新編電腦組裝與硬件維修從入門到精通
- FreeSWITCH Cookbook
- 微控制器的應用
- USB應用分析精粹:從設備硬件、固件到主機端程序設計
- 多媒體應用技術(第2版)
- 主板維修實踐技術