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	<title>Neurobot &#187; Clustering</title>
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	<link>http://neurobot.bio.auth.gr</link>
	<description>A computational neuroscience and neuroinformatics blog</description>
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		<title>Music perception triggers brain rhythms multiplexing: EEG experimental evidence</title>
		<link>http://neurobot.bio.auth.gr/2018/music-perception-triggers-brain-rhythms-multiplexing-eeg-experimental-evidence/</link>
		<comments>http://neurobot.bio.auth.gr/2018/music-perception-triggers-brain-rhythms-multiplexing-eeg-experimental-evidence/#comments</comments>
		<pubDate>Mon, 05 Feb 2018 16:40:04 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Documentation]]></category>
		<category><![CDATA[brain networks]]></category>
		<category><![CDATA[Clustering]]></category>
		<category><![CDATA[community detection]]></category>
		<category><![CDATA[modularity]]></category>
		<category><![CDATA[multiplexing]]></category>
		<category><![CDATA[music]]></category>
		<category><![CDATA[music neuroscience]]></category>
		<category><![CDATA[music perception]]></category>
		<category><![CDATA[network neuroscience]]></category>
		<category><![CDATA[Neural Networks]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=4001</guid>
		<description><![CDATA[A new study reports for the first time functional interactions among brain rhythms in music perception using a novel, elegant, multiplex-aware approach based on a community detection algorithm. The approach was motivated by recent trends in Network Science that focus on the multi-scaled character of complex network systems. A novel, elegant, multiplex-aware framework is introduced, [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2018/music-perception-triggers-brain-rhythms-multiplexing-eeg-experimental-evidence/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Musical NeuroPicks: a consumer-grade BCI for on-demand music streaming services</title>
		<link>http://neurobot.bio.auth.gr/2017/musical-neuropicks-a-consumer-grade-bci-for-on-demand-music-streaming-services/</link>
		<comments>http://neurobot.bio.auth.gr/2017/musical-neuropicks-a-consumer-grade-bci-for-on-demand-music-streaming-services/#comments</comments>
		<pubDate>Thu, 07 Sep 2017 11:19:34 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Documentation]]></category>
		<category><![CDATA[Brain Interfaces]]></category>
		<category><![CDATA[Clustering]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[music recommendation]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=3959</guid>
		<description><![CDATA[We investigated the possibility of using a machine-learning scheme in conjunction with commercial wearable EEG-devices for translating listener&#8217;s subjective experience of music into scores that can be used in popular on-demand music streaming services. Our study resulted into two variants, differing in terms of performance and execution time, and hence, subserving distinct applications in online [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2017/musical-neuropicks-a-consumer-grade-bci-for-on-demand-music-streaming-services/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Dominant-Sets clustering for spike sorting</title>
		<link>http://neurobot.bio.auth.gr/2013/dominant-sets-clustering-for-spike-sorting/</link>
		<comments>http://neurobot.bio.auth.gr/2013/dominant-sets-clustering-for-spike-sorting/#comments</comments>
		<pubDate>Wed, 30 Jan 2013 20:46:48 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Software]]></category>
		<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Clustering]]></category>
		<category><![CDATA[Sparse neurons]]></category>
		<category><![CDATA[Spike Sorting]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=3353</guid>
		<description><![CDATA[The decision about the actual number of active neurons is an open issue in spike sorting, with sparsely firing neurons and background activity the most influencing factors. Dominant-sets clustering algorithm is a graph-theoretical algorithmic procedure that successfully addresses this issue. The quality of grouping in the data is evaluated with the estimation of &#8216;cohesiveness&#8217;, i.e. [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2013/dominant-sets-clustering-for-spike-sorting/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Clustering Algorithms in Biomedical Research: A Review</title>
		<link>http://neurobot.bio.auth.gr/2012/clustering-algorithms-in-biomedical-research-a-review/</link>
		<comments>http://neurobot.bio.auth.gr/2012/clustering-algorithms-in-biomedical-research-a-review/#comments</comments>
		<pubDate>Thu, 20 Sep 2012 08:56:43 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Documentation]]></category>
		<category><![CDATA[Clustering]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=3283</guid>
		<description><![CDATA[Applications of clustering algorithms in biomedical research are ubiquitous. However, due to the diversity of cluster analysis, the diversity of critical elements underlying different clustering algorithms can be daunting. This paper presents an overview of the status quo of clustering algorithms and illustrates examples of biomedical applications based on cluster analysis. Access the review paper [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2012/clustering-algorithms-in-biomedical-research-a-review/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>In quest of the missing neuron: Spike sorting based on dominant-sets clustering</title>
		<link>http://neurobot.bio.auth.gr/2012/in-quest-of-the-missing-neuron-spike-sorting-based-on-dominant-sets-clustering/</link>
		<comments>http://neurobot.bio.auth.gr/2012/in-quest-of-the-missing-neuron-spike-sorting-based-on-dominant-sets-clustering/#comments</comments>
		<pubDate>Wed, 12 Sep 2012 11:18:51 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Documentation]]></category>
		<category><![CDATA[Clustering]]></category>
		<category><![CDATA[neural recordings]]></category>
		<category><![CDATA[Sparse neurons]]></category>
		<category><![CDATA[Spike Sorting]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=3276</guid>
		<description><![CDATA[Spike sorting algorithms aim at decomposing complex extracellular signals to independent events from single neurons in the electrode&#8217;s vicinity. The decision about the actual number of active neurons is still an open issue, with sparsely firing neurons and background activity the most influencing factors. We introduce a graph-theoretical algorithmic procedure that successfully resolves this issue. Dimensionality [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2012/in-quest-of-the-missing-neuron-spike-sorting-based-on-dominant-sets-clustering/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Spike sorting based on noise-assisted semi-supervised learning methodologies</title>
		<link>http://neurobot.bio.auth.gr/2012/spike-sorting-based-on-noise-assisted-semi-supervised-learning-methodologies/</link>
		<comments>http://neurobot.bio.auth.gr/2012/spike-sorting-based-on-noise-assisted-semi-supervised-learning-methodologies/#comments</comments>
		<pubDate>Sun, 08 Jan 2012 15:23:11 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Documentation]]></category>
		<category><![CDATA[Clustering]]></category>
		<category><![CDATA[Dimensionality Reduction]]></category>
		<category><![CDATA[Noise]]></category>
		<category><![CDATA[Spike Sorting]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=3076</guid>
		<description><![CDATA[SAN 2011 abstract in Neuroscience Letters 2011, vol. 500(Suppl.), e32-33.- DOI: http://dx.doi.org/10.1016/j.neulet.2011.05.161 Also, see presentation in SAN 2011]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2012/spike-sorting-based-on-noise-assisted-semi-supervised-learning-methodologies/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Spike Sorting: The First Step in Decoding the Brain</title>
		<link>http://neurobot.bio.auth.gr/2011/spike-sorting-the-first-step-in-decoding-the-brain/</link>
		<comments>http://neurobot.bio.auth.gr/2011/spike-sorting-the-first-step-in-decoding-the-brain/#comments</comments>
		<pubDate>Mon, 26 Dec 2011 10:19:14 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Documentation]]></category>
		<category><![CDATA[Brain Interfaces]]></category>
		<category><![CDATA[Clustering]]></category>
		<category><![CDATA[Spike Sorting]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=3061</guid>
		<description><![CDATA[An overview of the spike sorting problem and challenges in IEEE Signal Processing Magazine. You may read the article here: Gibson et al. Spike Sorting: The First Step in Decoding the Brain. Signal Processing Magazine, IEEE (2012) vol. 29 (1) pp. 124 &#8211; 143 For an extended review on available contemporary algorithms in the spike [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2011/spike-sorting-the-first-step-in-decoding-the-brain/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Performance comparison of extracellular spike sorting algorithms for single-channel recordings</title>
		<link>http://neurobot.bio.auth.gr/2011/performance-comparison-of-extracellular-spike-sorting-algorithms-for-single-channel-recordings/</link>
		<comments>http://neurobot.bio.auth.gr/2011/performance-comparison-of-extracellular-spike-sorting-algorithms-for-single-channel-recordings/#comments</comments>
		<pubDate>Wed, 07 Dec 2011 09:20:23 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Documentation]]></category>
		<category><![CDATA[Clustering]]></category>
		<category><![CDATA[Dimensionality Reduction]]></category>
		<category><![CDATA[Spike Sorting]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=3050</guid>
		<description><![CDATA[A review and comparative analysis paper of contemporary algorithms in the spike sorting domain, published in Journal of Neuroscience Methods. Wild J et al. “Performance comparison of extracellular spike sorting algorithms for single-channel recordings“. Journal of Neuroscience Methods, 2011, vol.203 (2), pp. 369-76; doi:10.1016/j.jneumeth.2011.10.013]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2011/performance-comparison-of-extracellular-spike-sorting-algorithms-for-single-channel-recordings/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Nev2lkit enhanced demo</title>
		<link>http://neurobot.bio.auth.gr/2011/nev2lkit-enhanced-demo/</link>
		<comments>http://neurobot.bio.auth.gr/2011/nev2lkit-enhanced-demo/#comments</comments>
		<pubDate>Thu, 19 May 2011 17:19:40 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Software]]></category>
		<category><![CDATA[Clustering]]></category>
		<category><![CDATA[Dimensionality Reduction]]></category>
		<category><![CDATA[Spike Sorting]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2962</guid>
		<description><![CDATA[An example of a spike sorting task using &#8216;nev2lkit enhanced&#8216; tool. Nev2lkit acts as a preprocessor for the extracellularly recorded data, extracting neural waveforms (i.e. spikes) from the continuous time series. Nev2lkit enhanced features user-customized optimization of the time-window used during the spike extraction procedure. Consequently, Nev2lkit employs Principal Component Analysis (PCA) in the spike [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2011/nev2lkit-enhanced-demo/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
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		</item>
		<item>
		<title>NASS: an empirical approach to spike sorting with overlap resolution based on a hybrid noise-assisted methodology</title>
		<link>http://neurobot.bio.auth.gr/2011/nass-an-empirical-approach-to-spike-sorting-with-overlap-resolution-based-on-a-hybrid-noise-assisted-methodology/</link>
		<comments>http://neurobot.bio.auth.gr/2011/nass-an-empirical-approach-to-spike-sorting-with-overlap-resolution-based-on-a-hybrid-noise-assisted-methodology/#comments</comments>
		<pubDate>Sat, 26 Feb 2011 07:39:05 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Documentation]]></category>
		<category><![CDATA[Clustering]]></category>
		<category><![CDATA[Dimensionality Reduction]]></category>
		<category><![CDATA[Noise]]></category>
		<category><![CDATA[Spike Sorting]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2881</guid>
		<description><![CDATA[Background noise and spike overlap pose problems in contemporary spike-sorting strategies. In this paper, both issues are addressed by a hybrid scheme that combines the robust representation of spike waveforms to facilitate the reliable identification of contributing neurons with efficient data learning to enable the precise decomposition of coactivations. A recently introduced manifold learning technique, [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2011/nass-an-empirical-approach-to-spike-sorting-with-overlap-resolution-based-on-a-hybrid-noise-assisted-methodology/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>An Expectation-Maximization tutorial in neural signal analysis</title>
		<link>http://neurobot.bio.auth.gr/2011/an-expectation-maximization-tutorial-in-neural-signal-processing/</link>
		<comments>http://neurobot.bio.auth.gr/2011/an-expectation-maximization-tutorial-in-neural-signal-processing/#comments</comments>
		<pubDate>Mon, 03 Jan 2011 11:18:06 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Clustering]]></category>
		<category><![CDATA[Spike Sorting]]></category>
		<category><![CDATA[Statistical Analysis]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2811</guid>
		<description><![CDATA[In this tutorial by Dr. Liam Paninski, the Expectation-Maximization (EM) algorithm is discussed and illustrated in a variety of neural examples. Key topics addressed: Example: Mixture models and spike sorting The method of bound optimization via auxiliary functions provides a useful alternative optimization technique The EM algorithm for maximizing the likelihood given hidden data may be derived [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2011/an-expectation-maximization-tutorial-in-neural-signal-processing/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Tutorial on Geometrical Data Analysis: Algorithms for Vectorial Pattern-Analysis</title>
		<link>http://neurobot.bio.auth.gr/2010/tutorial-on-geometrical-data-analysis-algorithms-for-vectorial-pattern-analysis/</link>
		<comments>http://neurobot.bio.auth.gr/2010/tutorial-on-geometrical-data-analysis-algorithms-for-vectorial-pattern-analysis/#comments</comments>
		<pubDate>Tue, 28 Dec 2010 12:10:14 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Clustering]]></category>
		<category><![CDATA[Dimensionality Reduction]]></category>
		<category><![CDATA[Neural Networks]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2787</guid>
		<description><![CDATA[By Dr. Nikolaos A. Laskaris The term ‘‘pattern’’, currently, encompasses the notion of a variety of data-forms the machines have to tackle with. Despite the fact that in early days it was used mostly for pictorial information, i.e. 2D-signals, now the same term stands almost for any output from a data-source. For instance, any digital-signal can be [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2010/tutorial-on-geometrical-data-analysis-algorithms-for-vectorial-pattern-analysis/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>SigTool: A MATLAB-based environment for sharing laboratory-developed software to analyze biological signals</title>
		<link>http://neurobot.bio.auth.gr/2009/sigtool-a-matlab-based-environment-for-sharing-laboratory-developed-software-to-analyze-biological-signals/</link>
		<comments>http://neurobot.bio.auth.gr/2009/sigtool-a-matlab-based-environment-for-sharing-laboratory-developed-software-to-analyze-biological-signals/#comments</comments>
		<pubDate>Sat, 16 May 2009 21:38:25 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Software]]></category>
		<category><![CDATA[Clustering]]></category>
		<category><![CDATA[Dimensionality Reduction]]></category>
		<category><![CDATA[Spike Sorting]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2316</guid>
		<description><![CDATA[Developed to run within MATLAB, sigTOOL provides a programming and analysis environment for processing neuroscience data. A graphical-user interface to this environment provides the end-user with a self-contained application for waveform and spike-train analysis. User-written extensions to this application can be added to the interface on-the-fly without the need to modify any of the existing [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2009/sigtool-a-matlab-based-environment-for-sharing-laboratory-developed-software-to-analyze-biological-signals/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Performance evaluation of PCA-based spike sorting algorithms</title>
		<link>http://neurobot.bio.auth.gr/2008/performance-evaluation-of-pca-based-spike-sorting-algorithms/</link>
		<comments>http://neurobot.bio.auth.gr/2008/performance-evaluation-of-pca-based-spike-sorting-algorithms/#comments</comments>
		<pubDate>Tue, 09 Sep 2008 07:14:06 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Documentation]]></category>
		<category><![CDATA[Clustering]]></category>
		<category><![CDATA[Neurophysiology]]></category>
		<category><![CDATA[Spike Sorting]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2230</guid>
		<description><![CDATA[Adamos DA, Kosmidis EK and Theophilidis G Deciphering the electrical activity of individual neurons from multi-unit noisy recordings is critical for understanding complex neural systems. A widely used spike sorting algorithm is being evaluated for single-electrode nerve trunk recordings. The algorithm is based on principal component analysis (PCA) for spike feature extraction. In the neuroscience [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2008/performance-evaluation-of-pca-based-spike-sorting-algorithms/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>FIND toolbox &#8211; Finding Information in Neural Data</title>
		<link>http://neurobot.bio.auth.gr/2008/find-toolbox-finding-information-in-neural-data/</link>
		<comments>http://neurobot.bio.auth.gr/2008/find-toolbox-finding-information-in-neural-data/#comments</comments>
		<pubDate>Thu, 21 Aug 2008 07:17:44 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Software]]></category>
		<category><![CDATA[Clustering]]></category>
		<category><![CDATA[Spike Sorting]]></category>
		<category><![CDATA[Statistical Analysis]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2220</guid>
		<description><![CDATA[A Matlab-based, open-source analysis toolbox for multiple-neuron recordings and network simulations. Currently the FIND-Toolbox accommodates import of multiple proprietary data formats, based on the Neuroshare Project . Physiological data from different acquisition systems and Network simulations Environments can now be compared using identical analysis methods. This allows verifying of both results across experiments and laboratories [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2008/find-toolbox-finding-information-in-neural-data/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Automatic spike detection and sorting using wavelets and super-paramagnetic clustering</title>
		<link>http://neurobot.bio.auth.gr/2006/automatic-spike-detection-and-sorting-using-wavelets-and-super-paramagnetic-clustering/</link>
		<comments>http://neurobot.bio.auth.gr/2006/automatic-spike-detection-and-sorting-using-wavelets-and-super-paramagnetic-clustering/#comments</comments>
		<pubDate>Wed, 02 Aug 2006 20:42:29 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Software]]></category>
		<category><![CDATA[Clustering]]></category>
		<category><![CDATA[Spike Sorting]]></category>
		<category><![CDATA[Wavelets]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2013</guid>
		<description><![CDATA[Wave_clus is a fast and unsupervised algorithm for spike detection and sorting. Although it gives a first unsupervised solution, this can be further modified according to the experimenter&#8217;s preference (semi-automatic sorting). By Rodrigo Quian Quiroga, Reader in Bioengineering, Dept. Engineering. University of Leicester, UK. The method combines the wavelet transform,which localizes distinctive spike features, with [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2006/automatic-spike-detection-and-sorting-using-wavelets-and-super-paramagnetic-clustering/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Expectation Maximization Theory</title>
		<link>http://neurobot.bio.auth.gr/2006/expectation-maximization-theory/</link>
		<comments>http://neurobot.bio.auth.gr/2006/expectation-maximization-theory/#comments</comments>
		<pubDate>Fri, 10 Mar 2006 13:15:02 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Documentation]]></category>
		<category><![CDATA[Clustering]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2001</guid>
		<description><![CDATA[An article on Expectation Maximization Theory, taken from the book &#8220;Biometric Authentication: A Machine Learning Approach&#8221;. The article/book-chapter addresses a data-clustering algorithm, called the expectation-maximization (EM) algorithm, when complete or partial information of observed data is made available. The book is written by M.W. Mak, S.Y. Kung, S.H. Lin. and the sample chapter is provided [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2006/expectation-maximization-theory/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>A Cluster Analysis Tutorial</title>
		<link>http://neurobot.bio.auth.gr/2006/a-cluster-analysis-tutorial/</link>
		<comments>http://neurobot.bio.auth.gr/2006/a-cluster-analysis-tutorial/#comments</comments>
		<pubDate>Wed, 08 Mar 2006 18:52:10 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Clustering]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2000</guid>
		<description><![CDATA[The term cluster analysis (first used by Tryon, 1939) encompasses a number of different algorithms and methods for grouping objects of similar kind into respective categories. A general question facing researchers in many areas of inquiry is how to organize observed data into meaningful structures, that is, to develop taxonomies. In other words cluster analysis [...]]]></description>
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		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>How Many Clusters? Which Clustering Method?</title>
		<link>http://neurobot.bio.auth.gr/2005/how-many-clusters-which-clustering-method/</link>
		<comments>http://neurobot.bio.auth.gr/2005/how-many-clusters-which-clustering-method/#comments</comments>
		<pubDate>Tue, 01 Feb 2005 09:19:59 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Documentation]]></category>
		<category><![CDATA[Clustering]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=1968</guid>
		<description><![CDATA[How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis. C. Fraley and A. E. Raftery Technical Report No. 329 Department of Statistics University of Washington Box 354322 Seattle, WA 98195-4322 USA We consider the problem of determining the structure of clustered data, without prior knowledge of the number of clusters or any other [...]]]></description>
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		<slash:comments>0</slash:comments>
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