15890486
Interval Data Clustering with Applications
Interval data is described by a group of variables, each of which contains a range of continuous values instead of the traditional single continuous or discrete value. Traditional data analysis simply replaces each interval by its representative (e.g., center or mean) and ignores the structure information of intervals. In this paper, we study the problem of clustering interval data using the modified or extended interval data dissimilarity measures. Our contributions are two-fold. First, we discuss various approaches for measuring the dissimilarities/distances between interval data, investigate the relations among them, and present a comprehensive experimental study on clustering interval data. We show that the extended interval data clustering achieves better performance than traditional ones and produces more meaningful and explanatory results. Second, we propose a two-stage approach for clustering interval data by exploiting the relations between the traditional distances and the modified distances. Experimental results show the effectiveness of our approach
206784011
The Relationships Among Various Nonnegative Matrix Factorization Methods for Clustering
The nonnegative matrix factorization (NMF) has been shown recently to be useful for clustering and various extensions and variations of NMF have been proposed recently. Despite significant research progress in this area, few attempts have been made to establish the connections between various factorization methods while highlighting their differences. In this paper we aim to provide a comprehensive study on matrix factorization for clustering. In particular, we present an overview and summary on various matrix factorization algorithms and theoretically analyze the relationships among them. Experiments are also conducted to empirically evaluate and compare various factorization methods. In addition, our study also answers several previously unaddressed yet important questions for matrix factorizations including the interpretation and normalization of cluster posterior and the benefits and evaluation of simultaneous clustering. We expect our study would provide good insights on matrix factorization research for clustering.
3018061
Instruction Balance and Its Relation to Program Energy Consumption
A computer consists of multiple components such as functional units, cache and main memory. At each moment of execution, a program may have a varied amount of work for each component. Recent development has exploited this imbalance to save energy by slowing the components that have a lower load. Example techniques include dynamic scaling and clock gating used in processors from Transmeta and Intel. Symmetrical to reconfiguring hardware is reorganizing software. We can alter program demand for different components by reordering program instructions. This paper explores the theoretical lower bound of energy consumption assuming that both a program and a machine are fully adjustable. It shows that a program with a balanced load always consumes less energy than the same program with uneven loads under the same execution speed. In addition, the paper examines the relation between energy consumption and program performance. It shows that reducing power is a different problem than that of improving performance. Finally, the paper presents empirical evidence showing that a program may be transformed to have a balanced demand in most parts of its execution.
8864863
Integrating Features from Different Sources for Music Information Retrieval
Efficient and intelligent music information retrieval is a very important topic of the 21st century. With the ultimate goal of building personal music information retrieval systems, this paper studies the problem of identifying "similar" artists using both lyrics and acoustic data. In this paper, we present a clustering algorithm that integrates features from both sources to perform bimodal learning. The algorithm is tested on a data set consisting of 570 songs from 53 albums of 41 artists using artist similarity provided by All Music Guide. Experimental results show that the accuracy of artist similarity classifiers can be significantly improved and that artist similarity can be efficiently identified.
14819927
Gene functional classification by semi-supervised learning from heterogeneous data
Gene function discovery is an important and interesting problem in computational analysis of microarray data. In this paper, we investigate the use of a semi-supervised learning algorithm for inferring gene functional classifications from heterogeneous data set consisting of DNA microarray expression measurements and phylogenetic profiles from whole-genome sequence compassions. The semisupervised learning approach aims at minimizing the disagreement between individual models built from each separate information source by employing a co-updating method and making use of both labeled and unlabeled data. Our results suggest that the semisupervised approach could be used for gene functional classification. The data sets and the program code used for the experiments can be accessed from our webpage.
1750917
A general model for clustering binary data
Clustering is the problem of identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity classes. This paper studies the problem of clustering binary data. This is the case for market basket datasets where the transactions contain items and for document datasets where the documents contain "bag of words". The contribution of the paper is three-fold. First a general binary data clustering model is presented. The model treats the data and features equally, based on their symmetric association relations, and explicitly describes the data assignments as well as feature assignments. We characterize several variations with different optimization procedures for the general model. Second, we also establish the connections between our clustering model with other existing clustering methods. Third, we also discuss the problem for determining the number of clusters for binary clustering. Experimental results show the effectiveness of the proposed clustering model.
16509721
A location system using asynchronous distributed sensors
This paper describes a location system using an asynchronous distributed sensor network. The sensors' clocks operate independently and are not explicitly synchronized. The motion of the source, or the object, between two emitted pulses will cause the inter-arrival time of these two pulses at each sensor to vary. This inter-arrival time is a function of the source location and motion and is independent of the clock offsets among sensors. Thus, the independently calculated time-difference-of-arrival (TDOA) of successive pulses at each sensor provides a reliable source of information to estimate the source position as well as its motion. The Cramer-Rao lower bound (CRLB) is derived to assess the performance of the proposed method and to show that its performance can approach that of a synchronous network given a sufficient number of sensors and moderate source movement. Some sub-optimal but effective source trajectory tracking methods are proposed which further relax the assumption of source motion between consecutive pulses. The simulation results show that the proposed estimation and tracking methods are quite good.
206736471
Wavelet spectrum and its characterization property for random processes
The wavelet spectrum of a random process comprises the variances of the wavelet coefficients of the process computed within each scale. This paper investigates the possibility of using the wavelet spectrum, obtained from a continuous wavelet transform (CWT), to uniquely represent the second-order statistical properties of random processes-particularly, stationary processes and long-memory nonstationary processes. As is well known, the Fourier spectrum of a stationary process is mathematically equivalent to the autocovariance function (ACF) and thus uniquely determines the second-order statistics of the process. This characterization property is shown to be possessed also by the wavelet spectrum under very mild regularity conditions that are easily satisfied by many widely used wavelets. It is also shown that under suitable regularity conditions, the characterization property remains valid for processes with stationary increments including 1/f noise.
2949388
Optimal k-Anonymity with Flexible Generalization Schemes through Bottom-up Searching
In recent years, a major thread of research on k-anonymity has focused on developing more flexible generalization schemes that produce higher-quality datasets. In this paper we introduce three new generalization schemes that improve on existing schemes, as well as algorithms enumerating valid generalizations in these schemes. We also introduce a taxonomy for generalization schemes and a new cost metric for measuring information loss. We present a bottom-up search strategy for finding optimal anonymizations. This strategy works particularly well when the value of k is small. We show the feasibility of our approach through experiments on real census data
200438
Nanoparticle-enhanced proton computed tomography: a Monte Carlo simulation study
Proton computed tomography (pCT) has the potential to improve the accuracy of proton treatment planning, which is currently based on X-ray computed tomography (XCT). However, at this time no method exists for pCT to selectively increase the contrast of tumor tissues with respect to normal tissues. One possibility to achieve this goal is to bind gold-nanoparticles, i.e., microscopic structures in the size range of 1 nm to several hundred nanometers, to the surface of tumor cells with cell-seeking antibodies conjugated to the surface of the nanoparticles. In order to test the suitability of this approach, we used the Monte Carlo transport code GEANT4, to provide simulated histories of 200 MeV protons going through a cylindrical water phantom with embedded inhomogeneities enhanced with traces of gold. The energy loss of several million protons traversing the cylinder from 180 different angles formed the basis for reconstructing the single proton CT image representing the relative volume electron density map of the phantom using an approximate filtered back projection algorithm. Our results demonstrate that with hundred nanoparticles of 100 nm diameters per tumor cell, it may be possible to enhance the visibility of tumors in pCT.
25228668
A joint estimation approach for two-tone image deblurring by blind deconvolution
A new statistical method is proposed for deblurring two-tone images, i.e., images with two unknown grey levels, that are blurred by an unknown linear filter. The key idea of the proposed method is to adjust a deblurring filter until its output becomes two tone. Two optimization criteria are proposed for the adjustment of the deblurring filter. A three-step iterative algorithm (TSIA) is also proposed to minimize the criteria. It is proven mathematically that by minimizing either of the criteria, the original (nonblurred) image, along with the blur filter, will be recovered uniquely (only with possible scale/shift ambiguities) at high SNR. The recovery is guaranteed not only for i.i.d. images but also for correlated and nonstationary images. It does not require a priori knowledge of the statistical parameters or the tone values of the original image; neither does it require a priori knowledge of the phase or other special information (e.g., FIR, symmetry, nonnegativity, etc.) about the blur filter. Numerical experiments are carried out to test the method on synthetic and real images.