Literature review of k- means clustering
From: Derek D.
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Many real-world systems can be studied in terms of pattern recognition tasks, so that proper use and understanding of machine learning methods in practical applications becomes essential. While many classification methods have been proposed, there is no consensus on which methods are more suitable for a given dataset. As a consequence, it is important to comprehensively compare methods in many possible scenarios. In this context, we performed a systematic comparison of 9 well-known clustering methods available in the R language assuming normally distributed data. In order to account for the many possible variations of data, we considered artificial datasets with several tunable properties number of classes, separation between classes, etc. In addition, we also evaluated the sensitivity of the clustering methods with regard to their parameters configuration. The results revealed that, when considering the default configurations of the adopted methods, the spectral approach tended to present particularly good performance.
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In this post, you will learn about K-Means clustering concepts with the help of fitting a K-Means model using Python Sklearn KMeans clustering implementation. Clustering represents a set of unsupervised machine learning algorithms belonging to different categories such as prototype-based clustering, hierarchical clustering, density-based clustering etc. K-means is one of the most popular clustering algorithm belong to prototype-based clustering category.
Sign in. Even though this clustering algorithm is fairly simple, it can look challenging to newcomers into the field. In this post, I try to tackle the process of k-Means Clustering with two different examples. The first example will focus more on the big picture and visualizing the process, while the second example focuses on the underlying calculation involved. The main difference betwe e n Supervised and Unsupervised learning algorithms is the absence of data labels in the latter.