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.
15267641
Estimating Joint Probabilities from Marginal Ones
Estimating joint probabilities plays an important role in many data mining and machine learning tasks. In this paper we introduce two methods, minAB and prodAB, to estimate joint probabilities. Both methods are based on a light-weight structure, partition support. The core idea is to maintain the partition support of itemsets over logically disjoint partitions and then use it to estimate joint probabilities of item-sets of higher cardinalitiess. We present extensive mathematical analyses on both methods and compare their performances on synthetic datasets. We also demonstrate a case study of using the estimation methods in Apriori algorithm for fast association mining. Moreover, we explore the usefulness of the estimation methods in other mining/learning tasks. Experimental results show the effectiveness of the estimation methods.
654543
A survey on wavelet applications in data mining
Recently there has been significant development in the use of wavelet methods in various data mining processes. However, there has been written no comprehensive survey available on the topic. The goal of this is paper to fill the void. First, the paper presents a high-level data-mining framework that reduces the overall process into smaller components. Then applications of wavelets for each component are reviewd. The paper concludes by discussing the impact of wavelets on data mining research and outlining potential future research directions and applications.
1016063
A comparative study on content-based music genre classification
Content-based music genre classification is a fundamental component of music information retrieval systems and has been gaining importance and enjoying a growing amount of attention with the emergence of digital music on the Internet. Currently little work has been done on automatic music genre classification, and in addition, the reported classification accuracies are relatively low. This paper proposes a new feature extraction method for music genre classification, DWCHs. DWCHs stands for Daubechies Wavelet Coefficient Histograms. DWCHs capture the local and global information of music signals simultaneously by computing histograms on their Daubechies wavelet coefficients. Effectiveness of this new feature and of previously studied features are compared using various machine learning classification algorithms, including Support Vector Machines and Linear Discriminant Analysis. It is demonstrated that the use of DWCHs significantly improves the accuracy of music genre classification.
26202322
Searching the web using composed pages
Given a user keyword query, current Web search engines return a list of pages ranked by their “goodness” with respect to the query. However, this technique misses results whose contents are distributed across multiple physical pages and are connected via hyperlinks and frames [3]. That is, it is often the case that no single page contains all query keywords. Li et al. [3] make a first step towards this problem by returning a tree of hyperlinked pages that collectively contain all query keywords. The limitation of this approach is that it operates at the page-level granularity, which ignores the specific context where the keywords are found within the pages. More importantly, it is cumbersome for the user to locate the most desirable tree of pages due to the amount of data in each page tree and a large number of page trees.
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.
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.
2644770
Pulse: A Dynamic Deadlock Detection Mechanism Using Speculative Execution
Deadlock can occur wherever multiple processes interact. Most existing static and dynamic deadlock detection tools focus on simple types of deadlock, such as those caused by incorrect ordering of lock acquisitions. In this paper, we propose Pulse, a novel operating system mechanism that dynamically detects various types of deadlock in application programs. Pulse runs as a system daemon. Periodically, it scans the system for processes that have been blocked for a long time (e.g., waiting on I/O events). To determine if these processes are deadlocked, Pulse speculatively executes them ahead to discover their dependences. Based on this information, it constructs a general resource graph and detects deadlock by checking if the graph contains cycles. The ability to look into the future allows Pulse to detect deadlocks involving consumable resources, such as synchronization semaphores and pipes, which no existing tools can detect. We evaluate Pulse by showing that it can detect deadlocks in the classical dining-philosophers and smokers problems. Furthermore, we show that Pulse can detect a well-known deadlock scenario, which is widely referred to as a denial-of-service vulnerability, in the Apache web server. Our results show that Pulse can detect all these deadlocks within three seconds, and it introduces little performance overhead to normal applications that do not deadlock.
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.
20811204
Asymptotic analysis of a fast algorithm for efficient multiple frequency estimation
Based on an asymptotic analysis of the contraction mapping (CM) method of Li and Kedem (1993), a bandwidth shrinkage rule is proposed for fast and accurate estimation of the frequencies of multiple sinusoids from noisy measurements. The CM frequency estimates are defined as the fixed points of a contractive mapping formed by the lag-one autocorrelation coefficient calculated from the output. of a parametric filter applied to the observed time series. With bandwidth parameters judiciously chosen according to the asymptotic analysis, the algorithm is shown to be able to accommodate possibly poor initial values of precision O(n/sup -1/3/) and converge to a final estimate whose accuracy is arbitrarily close to O(n/sup -3/2/), the optimal error rate for frequency estimation under the Gaussian assumption. The total computational complexity of the algorithm is shown to be O(n log n), which is comparable to that of n-point fast Fourier transform (FFT). A novelty in the asymptotic analysis is that it accommodates closely spaced frequencies by allowing not only the filter bandwidth but also the frequency separation to be functions of the sample size n. This enables an assessment of the accuracy of the frequency estimates for given bandwidths and initial values in situations where some or all of the frequencies are close to each other.
8716577
Blind identification and deconvolution of linear systems driven by binary random sequences
The problem of blind identification and deconvolution of linear systems with independent binary inputs is addressed. To solve the problem, a linear system is applied to the observed data and adjusted so as to produce binary outputs. It is proved that the system coincides with the inverse of the unknown system (with scale and shift ambiguities), whether it is minimum or nonminimum phase. These results are derived for nonstationary independent binary inputs of infinite or finite length. Based on these results, an identification method is proposed for parametric linear systems. It is shown that under some mild conditions, a consistent estimator of the parameter can be obtained by minimizing a binariness criterion for the output data. Unlike many other blind identification and deconvolution methods, this criterion handles nonstationary signals and does not utilize any moment information of the inputs. Three numerical examples are presented to demonstrate the effectiveness of the proposed method. >