[PDF] algorithm better than k means

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a clustering algorithm that is often considered to be superior to k-means clustering in many situations.
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  • What is the alternative to K-means?

    The K-means method is sensitive to outliers. An alternative to k-means clustering is the K-medoids clustering or PAM (Partitioning Around Medoids, Kaufman & Rousseeuw, 1990), which is less sensitive to outliers compared to k-means. Read more: Partitioning Clustering methods.

  • Is GMM better than K-means?

    The performance of GMM is better than that of K-means. The three clusters in GMM plot are closer to the original ones. Also, we compute the error rate (percentage of misclassified points) which should be the smaller the better. The Error rate of GMM is 0.0333, while that of K-means is 0.1067.

  • Is K-means ++ better than K-means?

    K-means can give different results on different runs. The k-means++ paper provides monte-carlo simulation results that show that k-means++ is both faster and provides a better performance, so there is no guarantee, but it may be better.

  • Top 12 Clustering Algorithms

    K-Means Algorithm. Mean-Shift Algorithm. DBSCAN Algorithm. Expectation-Maximization Clustering using Gaussian Mixture Models. Agglomerative Hierarchical Algorithm. Divisive Hierarchical Algorithm. OPTICS Algorithm (Ordering Points To Identify Cluster Structure)
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Alternatives to the k-means algorithm that find better clusterings

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