An attentive reader on the SenseClusters users list suggested the following three papers as being particularly good if one is interested in figuring out how to find the number of clusters automatically.
============================================================================ X-means: Extending K-means with Efficient Estimation of the Number of Clusters. Pelleg and Moore, ICML-2000 http://www.cs.cmu.edu/~dpelleg/download/kmeans.pdf With supporting commentary and code at: http://www.cs.cmu.edu/~dpelleg/kmeans.html Note that on his web page, Dan Pelleg mentions that the following similar but independent method predates X-means... An Efficient K-Means Clustering Algorithm, Alsabti, Ranka, and Singh First Workshop on High-Performance Data Mining, 1998 ftp://ftp.cise.ufl.edu/pub/faculty/ranka/cluster.ps.gz ============================================================================ Document Clustering with Cluster Refinement and Model Selection Capabilities Liu, Gong, Xu, and Zhu, SIGIR-2002 http://www.yow-now.com/xw/SIGIR02.pdf Abstract: In this paper, we propose a document clustering method that strives to achieve: (1) a high accuracy of document clustering, and (2) the capability of estimating the number of clusters in the document corpus (i.e. the model selection capability). To accurately cluster the given document corpus, we employ a richer feature set to represent each document, and use the Gaussian Mixture Model (GMM) together with the Expectation-Maximization (EM) algorithm to conduct an initial document clustering. From this initial result, we identify a set of discriminative features for each cluster, and refine the initially obtained document clusters by voting on the cluster label of each document using this discriminative feature set. This self-refinement process of discriminative feature identification and cluster label voting is iteratively applied until the convergence of document clusters. On the other hand, the model selection capability is achieved by introducing randomness in the cluster initialization stage, and then discovering a value C for the number of clusters N by which running the document clustering process for a fixed number of times yields sufficiently similar results. Performance evaluations exhibit clear superiority of the proposed method with its improved document clustering and model selection accuracies. The evaluations also demonstrate how each feature as well as the cluster refinement process contribute to the document clustering accuracy. [note the above also seems related to our interest in cluster labeling!] ============================================================================ Learning the k in k-means Hamerly and Elkan NIPS 2003 http://books.nips.cc/papers/files/nips16/NIPS2003_AA36.pdf Abstract: When clustering a dataset, the right number $k$ of clusters to use is often not obvious, and choosing k automatically is a hard algorithmic problem. In this paper we present a new algorithm for choosing k that is based on a new statistical test for the hypothesis that a subset of data follows a Gaussian distribution. The algorithm runs k-means with increasing k until the test fails to reject the hypothesis that the data assigned to each k-means center are Gaussian. We present results from experiments on synthetic and real-world data showing that the algorithm works well, and better than a recent method based on the BIC penalty for model complexity. ============================================================================ -- Ted Pedersen http://www.d.umn.edu/~tpederse ------------------------------------------------------- SF.Net email is Sponsored by the Better Software Conference & EXPO September 19-22, 2005 * San Francisco, CA * Development Lifecycle Practices Agile & Plan-Driven Development * Managing Projects & Teams * Testing & QA Security * Process Improvement & Measurement * http://www.sqe.com/bsce5sf _______________________________________________ senseclusters-users mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/senseclusters-users
