I have been studying an incremental clustering algorithm for a large set of data that exhibit an inherent dynamic behavior (that is new data can get added over time and some older data may get deleted too based on the current situation). In this scenario, let us assume, some clusters are formed at one particular time instant. Now when new data comes in, the processing should be limited to only handling this new data and and after metadata extraction, they should be clustered into the already existing clusters (if they are similar, otherwise new cluster must be formed). Now, I am really stumped with the problem of analyzing this algorithm. What kind of cluster validity measures can I use to evaluate the goodness of the clusters formed here? I have only started working in the area and so forgive me if this problem has an obvious answer that I have missed.
Thanks in advance. Really hope someone can help me with this.