Clustering is to classify unlabeled data into groups. It has been wellresearched for decades in many disciplines. Clustering in massive amount of astronomical data generated by multi-sensor networks has become an emerging new challenge; assumptions in many existing clustering algorithms are often violated in these domains. For example, K means implicitly assumes that underlying distribution of data is Gaussian. Such an assumption is not necessarily observed in astronomical data. Another problem is the determination of K, which is hard to decide when prior knowledge is lacking. While there has been work done on discovering the proper value for K given only the data, most existing works, such as X-means, G-means and PG-means, assume that the model is a mixture of Gaussians in one way or another. In this paper, we present a similarity-driven clustering approach for tackling large scale clustering problem. A similarity threshold T is used to constrain the search space of possible clustering models such that only those satisfying the threshold are accepted. This forces the search to: 1) explicitly avoid getting stuck in local minima, and hence the quality of models learned has a meaningful lower bound, and 2) discover a proper value for K as new clusters have to be formed if merging them into existing ones will violate the constraint given by the threshold. Experimental results on the UCI KDD archive and realistic simulated data generated for the Laser Interferometer Gravitational Wave Observatory (LIGO) suggest that such an approach is promising.
Lei, Hansheng; Tang, Lappoon R.; Iglesias, Juan R.; Mukherjee, Soma; and Mohanty, Soumya, "S-means: Similarity Driven Clustering and Its application in Gravitational-Wave Astronomy Data Mining" (2007). Computer Science Faculty Publications and Presentations. 15.
International Workshop on Knowledge Discovery from Ubiquitous Data Streams in conjunction with in conjunction with the 18th European Conference on Machine Learning (ECML’08) and the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD’08)