(Deciding on the ‘K’)
Here comes one of the crucial (and sometimes somewhat new zealand phone number data subjective) parts of K-Means: choosing the number of clusters, the famous ‘K’. The algorithm needs you to tell it how many groups you want to form. But what’s the right number? Two? Five? Twenty?
Choosing the right ‘K’ is crucial, as it directly affects the quality and usefulness of your results:
-
- If you choose a ‘K’ that’s too low: You risk creating very large and heterogeneous clusters. It’s like making only two piles in a Lego box: “red pieces” and “non-red pieces.” Within “non-red pieces,” you’d have everything (blue, yellow, wheels, shapes, etc.), and that group wouldn’t give you much useful information. In your data, you could be mixing pages with very different performance in the same cluster
.
- If you choose a ‘K’ that’s too low: You risk creating very large and heterogeneous clusters. It’s like making only two piles in a Lego box: “red pieces” and “non-red pieces.” Within “non-red pieces,” you’d have everything (blue, yellow, wheels, shapes, etc.), and that group wouldn’t give you much useful information. In your data, you could be mixing pages with very different performance in the same cluster
-
- If you choose too high a ‘K’: You can end up with transform your health and sales success very small clusters, sometimes with only one or two elements. It would be like making a pile for every tiny Lego color variation (“light red,” “dark red,” “orange-red,” etc.). These very specific groups can be difficult to interpret and generalize. You also run the risk of “overfitting,” creating groups that reflect the noise or specificities of your current data, but are not representative of real, stable patterns.
So how do we decide on the ‘K’?
There are several approaches:
- Business Insights:
types of customers (e.g., new, returning, VIPs), starting with K=3 is a good starting point. If you’re analyzing pages, you could think of categories like “informational,” “transactional,” “blog,” etc., and use that number as an initial guide
. - Elbow Method:
This is a more data-driven technique. It involves running K-Means multiple times, each gambler data with a different number of clusters (K=2, K=3, K=4, etc.). For each run, a metric that measures how compact the clusters are is calculated (usually the “within-cluster sum of squares” or WCSS). A graph is drawn with ‘K’ on the X-axis and WCSS on the Y-axis. As you increase ‘K’, the WCSS generally decreases (the clusters get smaller and more compact). What you’re looking for is the point on the graph where the curve starts to flatten, forming an “elbow.” That point is often considered a good balance between having enough clusters to capture the structure of the data and not having too many.