32095656
Analyzing Outliers Cautiously
Outliers are difficult to handle because some of them can be measurement errors, while others may represent phenomena of interest, something "significant" from the viewpoint of the application domain. Statistical and computational methods have been proposed to detect outliers, but further analysis of outliers requires much relevant domain knowledge. In our previous work (1994), we suggested a knowledge-based method for distinguishing between the measurement errors and phenomena of interest by modeling "real measurements" - how measurements should be distributed in an application domain. In this paper, we make this distinction by modeling measurement errors instead. This is a cautious approach to outlier analysis, which has been successfully applied to a medical problem and may find interesting applications in other domains such as science, engineering, finance, and economics.
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. >
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.
21789348
Evolutionary algorithms for grouping high dimensional Email data
Grouping problems arise in many industrial and medical applications; examples include bin packing, workshop layout design, and graph colouring. This type of problem has been successfully handled using Grouping Genetic Algorithms. However in problems where there are perhaps thousands of objects to be grouped, we have found that Genetic Algorithm approaches can run into problems. This paper continues our research into a method we have developed for decomposing a large number of objects into mutually exclusive subsets where within-group dependencies are high and between-group dependencies are low. The method uses an Evolutionary Algorithm approach but where the whole population is a solution to the grouping problem rather than considering many candidate solutions. This reduces the resource overheads during computer implementation and the results are promising when compared with standard statistical methods and a Hill Climbing algorithm, all applied to email log file data.
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.
14450831
Some New Parallel Mechanisms Containing the Planar Four-Bar Parallelogram
A parallelogram allows the output link to remain at a fixed orientation with respect to an input link, for which it acts as a unique role in the design of parallel mechanisms. In this paper, the unique role of a parallelogram is used completely to design some new parallel mechanisms with two to six degrees of freedom (DoFs). In these mechanisms, some with three DoFs possess the advantage of very high rotational capability and some with two DoFs have the translational output of a rigid body. More than that, the design concept is also applied first to some parallel mechanisms to improve the systems' rotational capability. The parallel mechanisms proposed in this paper have wide applications in industrial robots, simulators, micromanipulators, parallel kinematics machines, and any other manipulation devices in which high rotational capability and stiffness are needed. Especially, the paper provides new concepts of the design of novel parallel mechanisms and the improvement of rotational capability for such systems.
2422545
Probe-level measurement error improves accuracy in detecting differential gene expression
MOTIVATION
Finding differentially expressed genes is a fundamental objective of a microarray experiment. Numerous methods have been proposed to perform this task. Existing methods are based on point estimates of gene expression level obtained from each microarray experiment. This approach discards potentially useful information about measurement error that can be obtained from an appropriate probe-level analysis. Probabilistic probe-level models can be used to measure gene expression and also provide a level of uncertainty in this measurement. This probe-level measurement error provides useful information which can help in the identification of differentially expressed genes.


RESULTS
We propose a Bayesian method to include probe-level measurement error into the detection of differentially expressed genes from replicated experiments. A variational approximation is used for efficient parameter estimation. We compare this approximation with MAP and MCMC parameter estimation in terms of computational efficiency and accuracy. The method is used to calculate the probability of positive log-ratio (PPLR) of expression levels between conditions. Using the measurements from a recently developed Affymetrix probe-level model, multi-mgMOS, we test PPLR on a spike-in dataset and a mouse time-course dataset. Results show that the inclusion of probe-level measurement error improves accuracy in detecting differential gene expression.


AVAILABILITY
The MAP approximation and variational inference described in this paper have been implemented in an R package pplr. The MCMC method is implemented in Matlab. Both software are available from http://umber.sbs.man.ac.uk/resources/puma.