Clustering

Clustering

Clustering is an unsupervised machine learning method of identifying and grouping similar data points in larger datasets without concern for the specific outcome. Clustering (sometimes called cluster analysis) is usually used to classify data into structures that are more easily understood and manipulated. It’s worth keeping in mind that while it’s a popular strategy, clustering isn’t a monolithic term, as there are multiple algorithms that use cluster analysis with different mechanisms.

All this isn’t to say you should never use clustering, but rather that you should deploy it where and when it’ll give you the greatest impact and insights. Also, there are many situations in which clustering can not only give you a great starting point but shed light on important features of your data that can be enhanced with deeper analytics. These are just some of the times when you should use clustering:

For all the great things cluster analysis can do for your organization, there are just as many things that make it suboptimal when you’re looking for deep insights. Clustering by itself poses some important challenges that are inherent in the way you perform the analysis, and which makes it less than ideal for more complex ML and analytics-related tasks.

The biggest issue that comes up with most clustering methods is that while they’re great at initially separating your data into subsets, the strategies used are sometimes not necessarily related to the data itself, but to its positioning in relation to other points. K-means clustering (where datasets are separated into K groups based on randomly placed centroids), for instance, can have significantly different results depending on the number of groups you set and is generally not great when used with non-spherical clusters. Moreover, the fact that centroids are set at random also impacts the results and can lead to issues down the line.

Other algorithms can solve this problem, but not without a cost. Hierarchical clustering tends to produce more accurate results, but it requires significant computational power and is not ideal when you’re working with larger datasets. This method is also sensitive to outlier values and can produce inaccurate clusters as a result.

Perhaps most importantly, clustering isn’t a final step in your data discovery. Indeed, because it’s unsupervised and is more concerned with classification than deep insights, it is a great tool when you’re preparing your data for more intensive analysis.