5,255 research outputs found
Techniques for clustering gene expression data
Many clustering techniques have been proposed for the analysis of gene expression data obtained from microarray experiments. However, choice of suitable method(s) for a given experimental dataset is not straightforward. Common approaches do not translate well and fail to take account of the data profile. This review paper surveys state of the art applications which recognises these limitations and implements procedures to overcome them. It provides a framework for the evaluation of clustering in gene expression analyses. The nature of microarray data is discussed briefly. Selected examples are presented for the clustering methods considered
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Visual Representation of Text in Web Documents and Its Interpretation
This paper examines the uses of text and its representation on Web documents in terms of the challenges in its interpretation. Particular attention is paid to the significant problem of non-uniform representation of text. This non-uniformity is mainly due to the presence of semantically important text in image form as opposed to the standard encoded text. The issues surrounding text representation in Web documents are discussed in the context of colour perception and spatial representation. The characteristics of the representation of text in image form are examined and research towards interpreting these images of text is briefly described
Visual Representation of Text in Web Documents and Its Interpretation
This paper examines the uses of text and its representation on Web documents in terms of the challenges in its interpretation. Particular attention is paid to the significant problem of non-uniform representation of text. This non-uniformity is mainly due to the presence of semantically important text in image form as opposed to the standard encoded text. The issues surrounding text representation in Web documents are discussed in the context of colour perception and spatial representation. The characteristics of the representation of text in image form are examined and research towards interpreting these images of text is briefly described
On metastable conformational analysis of non-equilibrium biomolecular time series
We present a recently developed clustering method and specify it for the problem of identification of metastable conformations in nonequilibrium biomolecular time series. The approach is based on variational minimization of some novel regularized clustering functional. In context of conformational analysis, it allows one to combine the features of standard geometrical clustering techniques (like the Kmeans algorithm), dimension reduction methods (like principle component analysis), and dynamical machine learning approaches like hidden Markov models (HMMs). In contrast to the HMM-based approaches, no a priori assumptions about Markovianity of the underlying process and regarding probability distribution of the observed data are needed. The application of the computational framework is exemplified by means of conformational analysis of some penta-peptide torsion angle time series from a molecular dynamics simulation. Comparison of different versions of the presented algorithm is performed w.r.t. the metastability and geometrical resolution of the resulting conformations
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