17,728 research outputs found

    Exploratory Analysis of Functional Data via Clustering and Optimal Segmentation

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    We propose in this paper an exploratory analysis algorithm for functional data. The method partitions a set of functions into KK clusters and represents each cluster by a simple prototype (e.g., piecewise constant). The total number of segments in the prototypes, PP, is chosen by the user and optimally distributed among the clusters via two dynamic programming algorithms. The practical relevance of the method is shown on two real world datasets

    Nonparametric Hierarchical Clustering of Functional Data

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    In this paper, we deal with the problem of curves clustering. We propose a nonparametric method which partitions the curves into clusters and discretizes the dimensions of the curve points into intervals. The cross-product of these partitions forms a data-grid which is obtained using a Bayesian model selection approach while making no assumptions regarding the curves. Finally, a post-processing technique, aiming at reducing the number of clusters in order to improve the interpretability of the clustering, is proposed. It consists in optimally merging the clusters step by step, which corresponds to an agglomerative hierarchical classification whose dissimilarity measure is the variation of the criterion. Interestingly this measure is none other than the sum of the Kullback-Leibler divergences between clusters distributions before and after the merges. The practical interest of the approach for functional data exploratory analysis is presented and compared with an alternative approach on an artificial and a real world data set

    Analysis and automatic annotation of singer's postures during concert and rehearsal

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    Bodily movement of music performers is widely acknowledged to be a means of communication with the audience. For singers, where the necessity of movement for sound production is limited, postures, i.e. static positions of the body, may be relevant in addition to actual movements. In this study, we present the results of an analysis of a singer’s postures, focusing on differences in postures between a dress rehearsal without audience and a concert with audience. We provide an analysis based on manual annotation of postures and propose and evaluate methods for automatic annotation of postures based on motion sensing data, showing that automatic annotation is a viable alternative to manual annotation. Results furthermore suggest that the presence of an audience leads the singer to use more ‘open’ postures, and differentiate more between different postures. Also, speed differences of transitions from one posture to another are more pronounced in concert than during rehearsal

    An Application of Clustering Analysis to International Private Indebtedness

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    This paper presents a procedure for clustering analysis that combines Kohone’s Self organizing Feature Map (SOFM) and statistical schemes. The idea is to cluster the data in two stages: run SOFM and then minimize the segmentation dispersion. The advantages of proposed procedure will be illustrated through a synthetic experiment and a real macroeconomic problem. The procedure is then used to explore the relationship between private indebtedness and some macroeconomic variables commonly used to measure macroeconomic performance. The experiences of thirty-nine countries in the early nineties are analyzed. The procedure outperformed others clustering techniques in the job of identifying consistent groups of countries from the economic and statistical viewpoints. It found out similarities in different countries concerning their respective levels of private indebtedness when added to well accepted parameters to measure macroeconomic performance.Vector quantization, Clustering, Self-Organizing Feature Map,Macroeconomic Performance, Private Indebtedness.

    The application of clustering analysis to international private indebtedness

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    The main goal of this paper is to apply a combination of statistical and connectionist schemes to examine, via clustering analysis, private indebtedness in different countries. Thirty-nine such experiences are used. The relationship between private debts and some macroeconomic variables are discussed in some detail. The clustering performance is improved by taking advantage of specific properties and capacities of each method. The procedures are also applied to a controlled numerical example.

    Batch and median neural gas

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    Neural Gas (NG) constitutes a very robust clustering algorithm given euclidian data which does not suffer from the problem of local minima like simple vector quantization, or topological restrictions like the self-organizing map. Based on the cost function of NG, we introduce a batch variant of NG which shows much faster convergence and which can be interpreted as an optimization of the cost function by the Newton method. This formulation has the additional benefit that, based on the notion of the generalized median in analogy to Median SOM, a variant for non-vectorial proximity data can be introduced. We prove convergence of batch and median versions of NG, SOM, and k-means in a unified formulation, and we investigate the behavior of the algorithms in several experiments.Comment: In Special Issue after WSOM 05 Conference, 5-8 september, 2005, Pari

    Analyzing and clustering neural data

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    This thesis aims to analyze neural data in an overall effort by the Charles Stark Draper Laboratory to determine an underlying pattern in brain activity in healthy individuals versus patients with a brain degenerative disorder. The neural data comes from ECoG (electrocorticography) applied to either humans or primates. Each ECoG array has electrodes that measure voltage variations which neuroscientists claim correlates to neurons transmitting signals to one another. ECoG differs from the less invasive technique of EEG (electroencephalography) in that EEG electrodes are placed above a patients scalp while ECoG involves drilling small holes in the skull to allow electrodes to be closer to the brain. Because of this ECoG boasts an exceptionally high signal-to-noise ratio and less susceptibility to artifacts than EEG [6]. While wearing the ECoG caps, the patients are asked to perform a range of different tasks. The tasks performed by patients are partitioned into different levels of mental stress i.e. how much concentration is presumably required. The specific dataset used in this thesis is derived from cognitive behavior experiments performed on primates at MGH (Massachusetts General Hospital). The content of this thesis can be thought of as a pipelined process. First the data is collected from the ECoG electrodes, then the data is pre-processed via signal processing techniques and finally the data is clustered via unsupervised learning techniques. For both the pre-processing and the clustering steps, different techniques are applied and then compared against one another. The focus of this thesis is to evaluate clustering techniques when applied to neural data. For the pre-processing step, two types of bandpass filters, a Butterworth Filter and a Chebyshev Filter were applied. For the clustering step three techniques were applied to the data, K-means Clustering, Spectral Clustering and Self-Tuning Spectral Clustering. We conclude that for pre-processing the results from both filters are very similar and thus either filter is sufficient. For clustering we conclude that K- means has the lowest amount of overlap between clusters. K-means is also the most time-efficient of the three techniques and is thus the ideal choice for this application.2016-10-27T00:00:00
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