Effective Strategies for Addressing K-Means Initialization Challenges

Effective Strategies for Addressing K-Means Initialization Challenges.


Author(s): Flo

Originally published on Towards AI.

Using n_init and K-Means++
image by Flo

K-Means is a widely-used clustering algorithm in Machine Learning, boasting numerous benefits but also presenting significant challenges. In this article, we delve into its limitations and offer straightforward solutions to address them.

K-Means is a clustering algorithm that partitions data into K clusters. It initializes K centroids randomly and then assigns each data point to the nearest centroid. Centroids are recalculated based on the mean of assigned points, and the process repeats until convergence.

I illustrate it below with t-distributed stochastic neighbor embedding, a dimension reduction technique. Each cluster is represented by a color.

K-Means clusters via T-SNE, image by Flo

K-Means… Read the full blog for free on Medium.

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Published via Towards AI


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