[PDF] python clustering unknown number of clusters

Overview

In Data Science, one of the first phases of discovery is determining what is in the data. The less we know about the data, the more we need algorithms that will help us discover more about it. If we graph our data and can see well-defined clusters, then we’ve got algorithms where we simply supply the numbers of clusters ourselves. But, if we have a...

Clustering Concepts

Clustering is similar to classification.To classify, we need to know into what categories we want to put the data. But, we can use clustering when we’re not sure what those classifications might be. In that case, it’s up to the algorithm to find the patterns and to create the clusters. Different algorithms will produce different clusters. So, it’s ...

Calculating Distances Or Similarities

Simply put, there are four methods we can use for deciding “how close” a cluster and a nearby point might be: 1. In the first, we look at the closest pointin the cluster to the outside point. 2. In the second, we look at the farthest pointin the cluster to the outside point. 3. The third method asks us to determine the average distancesbetween all ...

Algorithms

There are a number of ways of achieving clustering: 1. Compactness takes a representative point and its parameters. The more similar the other points in the cluster are, the more compactthe cluster is. 2. Connectivityworks on the idea that objects that are nearby are more related than objects that are farther away. 3. Linearity is about the kinds o...

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How do you find clusters using k-means?

One way to do it is to run k-means with large k (much larger than what you think is the correct number), say 1000. then, running mean-shift algorithm on the these 1000 point (mean shift uses the whole data but you will only "move" these 1000 points). mean shift will find the amount of clusters then.

Is clustering easy to use?

And it is really easy to use, you can run quite complex clustering algorithm with a couple of lines of code. Some of them require the number of clusters beforehand, but it is not the case of all of them. For example, hierarchical clustering can be used to obtain any number of clusters (there are nice explanations on this page ).

Do clustering algorithms need to pre-specify the number of clusters?

Clustering algorithms that require you to pre-specify the number of clusters are a small minority. There are a huge number of algorithms that don't. They are hard to summarize; it's a bit like asking for a description of any organisms that aren't cats. Clustering algorithms are often categorized into broad kingdoms:

How do you find the optimal number of clusters in a dendrogram?

We find the optimal number of clusters by finding the longest unbroken line in the dendrogram, creating a vertical line at that point, and counting the number of crossed lines. In the example above, we find 2 clusters. 4.2. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) DBSCAN is a density-based clustering algorithm.

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