@conference {13179,
	title = {Semi-Parametric Model-Based Clustering for DNA Microarray Data},
	booktitle = {Pattern Recognition, 2006. ICPR 2006. 18th International Conference on},
	volume = {3},
	year = {2006},
	month = {2006//00/0},
	pages = {324 - 327},
	abstract = {Various clustering methods have been proposed for the analysis of gene expression data, but conventional clustering algorithms have several critical limitations; how to set parameters such as number of clusters, initial cluster centers, and so on. In this paper, we propose a semi-parametric model-based clustering algorithm in which the underlying model is a mixture of Gaussian. Each gene expression data builds a Gaussian kernel, and the uncertainty of microarray data is naturally integrated in the data representation. Our algorithm provides a principled method to automatically determine parameters - number of components in the mixture, mean, covariance, and weight of each Gaussian - by mean-shift procedure (Comaniciu and Meer, 1999) and curvature fitting. After the initialization, expectation maximization (EM) algorithm is employed for clustering to achieve maximum likelihood (ML). The performance of our algorithm is compared with standard EM algorithm using real data as well as synthetic data},
	keywords = {clustering;, clustering;DNA;Gaussian, computing;genetics;pattern, data;Gaussian, data;maximum, DNA, Expression, fitting;data, kernel;Gaussian, likelihood;mean-shift, maximization;gene, Microarray, mixtures;curvature, model-based, procedure;semiparametric, processes;biology, representation;expectation},
	doi = {10.1109/ICPR.2006.1044},
	author = {Han,Bohyung and Davis, Larry S.}
}
@conference {13239,
	title = {Mean-shift analysis using quasiNewton methods},
	booktitle = {Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on},
	volume = {2},
	year = {2003},
	month = {2003/09//},
	pages = {II - 447-50 vol.3 - II - 447-50 vol.3},
	abstract = {Mean-shift analysis is a general nonparametric clustering technique based on density estimation for the analysis of complex feature spaces. The algorithm consists of a simple iterative procedure that shifts each of the feature points to the nearest stationary point along the gradient directions of the estimated density function. It has been successfully applied to many applications such as segmentation and tracking. However, despite its promising performance, there are applications for which the algorithm converges too slowly to be practical. We propose and implement an improved version of the mean-shift algorithm using quasiNewton methods to achieve higher convergence rates. Another benefit of our algorithm is its ability to achieve clustering even for very complex and irregular feature-space topography. Experimental results demonstrate the efficiency and effectiveness of our algorithm.},
	keywords = {analysis;, classification;, clustering, clustering;, Convergence, density, estimation;, feature-space, image, irregular, iterative, Mean-shift, method;, methods;, Newton, nonparametric, pattern, procedure;, quasiNewton, rates;, segmentation;, technique;, topography;},
	doi = {10.1109/ICIP.2003.1246713},
	author = {Yang,C. and Duraiswami, Ramani and DeMenthon,D. and Davis, Larry S.}
}
@article {15613,
	title = {An efficient k-means clustering algorithm: analysis and implementation},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {24},
	year = {2002},
	month = {2002/07//},
	pages = {881 - 892},
	abstract = {In k-means clustering, we are given a set of n data points in d-dimensional space R<sup>d</sup> and an integer k and the problem is to determine a set of k points in Rd, called centers, so as to minimize the mean squared distance from each data point to its nearest center. A popular heuristic for k-means clustering is Lloyd{\textquoteright}s (1982) algorithm. We present a simple and efficient implementation of Lloyd{\textquoteright}s k-means clustering algorithm, which we call the filtering algorithm. This algorithm is easy to implement, requiring a kd-tree as the only major data structure. We establish the practical efficiency of the filtering algorithm in two ways. First, we present a data-sensitive analysis of the algorithm{\textquoteright}s running time, which shows that the algorithm runs faster as the separation between clusters increases. Second, we present a number of empirical studies both on synthetically generated data and on real data sets from applications in color quantization, data compression, and image segmentation},
	keywords = {algorithm;color, algorithm;image, algorithm;kd-tree;mean, analysis;filtering, clustering, clustering;, compression;data, distance;covariance, Lloyd, matrices;filtering, quantization;data, segmentation;k-means, squared, structure;data-sensitive, theory;pattern},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2002.1017616},
	author = {Kanungo,T. and Mount, Dave and Netanyahu,N. S and Piatko,C. D and Silverman,R. and Wu,A. Y}
}
