{"id":1289,"date":"2017-07-03T23:34:34","date_gmt":"2017-07-03T23:34:34","guid":{"rendered":"http:\/\/intelligentonlinetools.com\/blog\/?p=1289"},"modified":"2017-07-07T10:36:14","modified_gmt":"2017-07-07T10:36:14","slug":"algorithms-metrics-and-online-tool-for-clustering","status":"publish","type":"post","link":"http:\/\/intelligentonlinetools.com\/blog\/2017\/07\/03\/algorithms-metrics-and-online-tool-for-clustering\/","title":{"rendered":"Algorithms, Metrics and Online Tool for Clustering"},"content":{"rendered":"

One of the key techniques of exploratory data mining is clustering<\/b> \u2013 separating instances into distinct groups based on some measure of similarity. [1] In this post we will review how we can do clustering, evaluate and visualize results using online ML Sandbox<\/a> tool from this website. This tool allows to run some machine learning algorithms without coding and setup\/install. The following components will be explored:<\/p>\n

Clustering Algorithms<\/strong>
\nK-means Clustering Algorithm<\/strong> – is well known algorithm as the idea of this algorithm goes back to 1957. [2] The algorithm requires to input number of clusters and data. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean.[2]. Below are shown results of K-means clustering of Iris dataset (only 2 dimensions shown) and clustering result for S1 dataset (see dataset section for more details).<\/p>\n

\"\"
k-means clustering Iris dataset<\/figcaption><\/figure>
\nFig 1. K-means clustering of Iris dataset<\/p>\n

\"\"
\nFig 2. K-means clustering of S1 dataset<\/p>\n

Affinity Propagation<\/strong> – performs affinity propagation clustering of data. In statistics and data mining, affinity propagation (AP) is a clustering algorithm based on the concept of “message passing” between data points. Unlike clustering algorithms such as k-means or k-medoids, affinity propagation does not require the number of clusters to be determined or estimated before running the algorithm. Similar to k-medoids, affinity propagation finds “exemplars”, members of the input set that are representative of clusters.[3]<\/p>\n

Hierarchical clustering (HC)<\/strong> – (also called hierarchical cluster analysis or HCA) is a method of cluster analysis which seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two types:<\/p>\n