31090021
A genetic k-medoids clustering algorithm
We propose a hybrid genetic algorithm for k-medoids clustering. A novel heuristic operator is designed and integrated with the genetic algorithm to fine-tune the search. Further, variable length individuals that encode different number of medoids (clusters) are used for evolution with a modified Davies-Bouldin index as a measure of the fitness of the corresponding partitionings. As a result the proposed algorithm can efficiently evolve appropriate partitionings while making no a priori assumption about the number of clusters present in the datasets. In the experiments, we show the effectiveness of the proposed algorithm and compare it with other related clustering methods.
6104018
A framework for modelling virus gene expression data
Short, high-dimensional, Multivariate Time Series (MTS) data are common in many fields such as medicine, finance and science, and any advance in modelling this kind of data would be beneficial. Nowhere is this truer than functional genomics where effective ways of analysing gene expression data are urgently needed. Progress in this area could help obtain a “global” view of biological processes, and ultimately lead to a great improvement in the quality of human life. We present a computational framework for modelling this type of data, and report experimental results of applying this framework to the analysis of gene expression data in the virology domain. The framework contains a three-step modelling strategy: correlation search, variable grouping, and short MTS modelling. Novel research is involved in each step which has been individually tested on different real-world datasets in engineering and medicine. This is the first attempt to integrate all these components into a coherent computational framework, and test the framework on a very challenging application area, producing promising results.
36513177
A Bayesian network approach to explaining time series with changing structure
Many examples exist of multivariate time series where dependencies between variables change over time. If these changing dependencies are not taken into account, any model that is learnt from the data will average over the different dependency structures. Paradigms that try to explain underlying processes and observed events in multivariate time series must explicitly model these changes in order to allow non-experts to analyse and understand such data. In this paper we have developed a method for generating explanations in multivariate time series that takes into account changing dependency structure. We make use of a dynamic Bayesian network model with hidden nodes. We introduce a representation and search technique for learning such models from data and test it on synthetic time series and real-world data from an oil refinery, both of which contain changing underlying structure. We compare our method to an existing EM-based method for learning structure. Results are very promising for our method and we include sample explanations, generated from models learnt from the refinery dataset.
39184835
Improved Robustness in Time Series Analysis of Gene Expression Data by Polynomial Model Based Clustering
Microarray experiments produce large data sets that often contain noise and considerable missing data. Typical clustering methods such as hierarchical clustering or partitional algorithms can often be adversely affected by such data. This paper introduces a method to overcome such problems associated with noise and missing data by modelling the time series data with polynomials and using these models to cluster the data. Similarity measures for polynomials are given that comply with commonly used standard measures. The polynomial model based clustering is compared with standard clustering methods under different conditions and applied to a real gene expression data set. It shows significantly better results as noise and missing data are increased.
40113853
A Framework for Modelling Short, High-Dimensional Multivariate Time Series: Preliminary Results in Virus Gene Expression Data Analysis
Short, high-dimensional Multivariate Time Series (MTS) data are common in many fields such as medicine, finance and science, and any advance in modelling this kind of data would be beneficial. Nowhere is this more true than functional genomics where effective ways of analysing gene expression data are urgently needed. Progress in this area could help obtain a "global" view of biological processes, and ultimately lead to a great improvement in the quality of human life. We present a computational framework for modelling this type of data, and report preliminary experimental results of applying this framework to the analysis of gene expression data in the virology domain. The framework contains a threestep modelling strategy: correlation search, variable grouping, and short MTS modelling. Novel research is involved in each step which has been individually tested on different real-world datasets in engineering and medicine. This is the first attempt to integrate all these components into a coherent computational framework, and test the framework on a very challenging application area, which has produced promising results.
16058917
Learning Dynamic Bayesian Networks from Multivariate Time Series with Changing Dependencies
Many examples exist of multivariate time series where dependencies between variables change over time. If these changing dependencies are not taken into account, any model that is learnt from the data will average over the different dependency structures. Paradigms that try to explain underlying processes and observed events in multivariate time series must explicitly model these changes in order to allow non-experts to analyse and understand such data. In this paper we have developed a method for generating explanations in multivariate time series that takes into account changing dependency structure. We make use of a dynamic Bayesian network model with hidden nodes. We introduce a representation and search technique for learning such models from data and test it on synthetic time series and real-world data from an oil refinery, both of which contain changing underlying structure. We compare our method to an existing EM-based method for learning structure. Results are very promising for our method and we include sample explanations, generated from models learnt from the refinery dataset.
3266821
A Least Biased Fuzzy Clustering Method
A new operational definition of cluster is proposed, and a fuzzy clustering algorithm with minimal biases is formulated by making use of the maximum entropy principle to maximize the entropy of the centroids with respect to the data points (clustering entropy). The authors make no assumptions on the number of clusters or their initial positions. For each value of an adimensional scale parameter /spl beta/', the clustering algorithm makes each data point iterate towards one of the cluster's centroids, so that both hard and fuzzy partitions are obtained. Since the clustering algorithm can make a multiscale analysis of the given data set one can obtain both hierarchy and partitioning type clustering. The relative stability with respect to /spl beta/' of each cluster structure is defined as the measurement of cluster validity. The authors determine the specific value of /spl beta/' which corresponds to the optimal positions of cluster centroids by minimizing the entropy of the data points with respect to the centroids (clustered entropy). Examples are given to show how this least biased method succeeds in getting perceptually correct clustering results. >
206530429
Point representation augmented to surface reconstruction in image-based VR
In this paper we propose a hybrid representation of environment models by a point representation augmented to geometric representation of surface polygons reconstructed from multiple reference images, through which a real time walkthrough of a complex environment can be achieved. By the method, we start from classification of pixels of the source images into two categories, corresponding respectively to the planar and non-planar surfaces in 3D space. For the pixels corresponding to the planar surfaces, the plane coefficients are reconstructed and all their appearances in the reference images are merged to form uniformly sampled texture images by a comparison of sampling rate and resampling. For the pixels corresponding to the non-planar surfaces, a point representation is applied and the redundant pixels are removed again through sampling rate comparison. The remained pixels are organized by OBB-tree according to their space coordinates. At the same time, the holes that are unable to be captured by all the reference images are pre-filled in the preprocessing phase so that the probability of hole appearance in walkthrough is greatly reduced. Under this hybrid representation, texture mapping and point warping are employed to render the novel views, to take full advantages of the acceleration utility of graphics hardware.
17082553
HyPE: hybrid power estimation for IP-based systems-on-chip
In this paper, we present a novel power estimation scheme for programmable systems consisting of predesigned datapath and memory components. The proposed hybrid methodology yields highly accurate estimates within short runtimes by combining high-level simulation with analytical macromodeling of circuit characteristics. The kernel of our methodology is a simulation-free power estimation scheme for memoryless datapaths comprising several IP blocks connected in fixed topologies. The outer shell of our hybrid scheme is a functional simulation, which is performed only on the interfaces between memoryless components and memory blocks. This simulation accurately captures the control signals that affect the flow of data and, consequently, the utilization and power dissipation of hardware. Experimental results validate the accuracy and efficiency of our methodology. We applied our static power estimation kernel to signal processing and data encryption datapaths. For designs of up to 576 IP blocks, the average error of our power estimates is 7.3% in comparison with switch-level simulation results. We implemented our hybrid scheme into a power estimation tool, called HYPE, and used it to explore various architectural alternatives in the design of a 256-state Viterbi decoder and a Rijndael encryptor. For designs with about 1 million transistors, our estimator terminates within seconds. Compared with commercial state-of-the-art gate-level power estimators, our proposed methodology is up to 1000 times faster with 5.4% deviation on average.
7819313
Constrained Fairing for Meshes
In this paper, we present a novel fairing algorithm for the removal of noise from uniform triangular meshes without shrinkage and serious distortion. The key feature of this algorithm is to keep all triangle centers invariant at each smoothing step by including some constraints in the energy minimization functional. The constrained functional is then minimized efficiently using an iterative method. Further, we apply this smoothing technique to a multiresolution representation to remove arbitrary levels of detail. A volume-preserving decimation algorithm is presented to generate the multiresolution representation. The experimental results demonstrate the combined algorithm's stability and efficiency.
62878
Single-hop probing asymptotics in available bandwidth estimation: sample-path analysis
In this paper, we take the sample-path approach in analyzing the asymptotic behavior of single-hop bandwidth estimation under bursty cross-traffic and show that these results are provably different from those observed under fluid models of prior work. This difference, which we call the probing bias, is one of the previously unknown factors that can cause measurement inaccuracies in available bandwidth estimation. We present an analytical formulation of "packet probing," based on which we derive several major properties of the probing bias. We then experimentally observe the <i>probing bias</i> and investigate its quantitative relationship to several deciding factors such as probing packet size, probing train length, and cross-traffic burstiness. Both our analytical and experimental results show that the probing bias vanishes as the packet-train length or packet size increases. The vanishing rate is decided by the burstiness of cross-traffic.