Premium
Simultaneous Unsupervised Learning of Disparate Clusterings
Author(s) -
Jain Prateek,
Meka Raghu,
Dhillon Inderjit S.
Publication year - 2008
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.10007
Subject(s) - computer science , cluster analysis , artificial intelligence , machine learning , domain (mathematical analysis) , data mining , function (biology) , mathematics , mathematical analysis , evolutionary biology , biology
Most clustering algorithms produce a single clustering for a given dataset 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 methods do not perform well on many real high‐dimensional datasets. We propose a new regularized factorial‐learning framework that is more suitable for capturing the notion of disparate clusterings in modern, high‐dimensional datasets. Furthermore, we provide kernelized version of both of our algorithms. The resulting algorithms do well in uncovering multiple clusterings, and are much improved over existing methods. We evaluate our methods on two real‐world datasets—a music dataset from the text‐mining domain, and a portrait dataset from the computer‐vision domain. Our methods achieve a substantially higher accuracy than existing factorial learning as well as traditional clustering algorithms. Copyright © 2008 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 1: 000‐000, 2008