You had a knowledge interpretation problem, so that you tried clustering. Now you may have a cluster interpretation problem! There was a suspicion that patterns might exist in the info. Reasonably, the hope was that adding some structure through unsupervised learning would lend some insights. Clusters are the go-to tool for locating structure. Thus, you launched into your journey. You spend considerable money on computing. You invest plenty of sweat in twiddling with cluster tuning parameters. Simply to make certain, you are trying just a few algorithms. But at the tip of the day you’re left with rainbow plots of clustered data that may need some meaning — just possibly — when you squint hard enough. You go home with an uneasy suspicion that it was all for naught. Sadly, this is just too often the case. Why should this be though?
Failing to supply value in a clustering project often comes from just a few causes: poor understanding of the info, too little attention on the specified consequence, and poor tool selection. We’ll walk through each of those in turn. To motivate the discussion, it’s illuminating to know the explanations clustering techniques exist. To get there, we’ll review what clustering is and just a few of the issues that prompted the event of clustering techniques.
