Simultaneous Unsupervised Learning of Disparate Clusterings

Most clustering algorithms produce a single clustering for a given data set even when the data can be clustered naturally in multiple ways. In this paper, we address the difficult problem of uncovering disparate clusterings from the data in a totally unsupervised manner. We propose two new approaches for this problem. In the first approach we aim to find good clusterings of the data that are also decorrelated with one another. To this end, we give a new and tractable characterization of decorrelation between clusterings, and present an objective function to capture it. We provide an iterative "decorrelated" k-means type algorithm to minimize this objective function. In the second approach, we model the data as a sum of mixtures and associate each mixture with a clustering. This approach leads us to the problem of learning a convolution of mixture distributions. Though the latter problem can be formulated as one of factorial learning [8, 13, 16], the existing formulations and...

• Type: Conference
• Year: 2008
• Where: SDM
• Authors: Prateek Jain, Raghu Meka, Inderjit S. Dhillon
• Keywords: Clustering Algorithms, Data Mining, Data Set, Factorial Learning, SDM 2008