217251
On combining multiple clusterings
Many problems can be reduced to the problem of combining multiple clusterings. In this paper, we first summarize different application scenarios of combining multiple clusterings and provide a new perspective of viewing the problem as a categorical clustering problem. We then show the connections between various consensus and clustering criteria and discuss the complexity results of the problem. Finally we propose a new method to determine the final clustering. Experiments on kinship terms and clustering popular music from heterogeneous feature sets show the effectiveness of combining multiple clusterings.
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.
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.
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.
9813108
CoFD : An Algorithm for Non-distance Based Clustering in High Dimensional Spaces
The clustering problem, which aims at identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity clusters, has been widely studied. Traditional clustering algorithms use distance functions to measure similarity and are not suitable for high dimensional spaces. In this paper, we propose CoFD algorithm, which is a non-distance based clustering algorithm for high dimensional spaces. Based on the maximum likelihood principle, CoFD is to optimize parameters to maximize the likelihood between data points and the model generated by the parameters. Experimental results on both synthetic data sets and a real data set show the efficiency and effectiveness of CoFD.
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.
6665649
My Brain is Full: When More Memory Helps
We consider the problem of finding good finite-horizon policies for POMDPs under the expected reward metric. The policies considered are free finite-memory policies with limited memory; a policy is a mapping from the space of observation-memory pairs to the space of action-memory pairs (the policy updates the memory as it goes), and the number of possible memory states is a parameter of the input to the policy-finding algorithms. The algorithms considered here are preliminary implementations of three search heuristics: local search. simulated annealing, and genetic algorithms. We compare their outcomes to each other and to the optimal policies for each instance. We compare run times of each policy and of a dynamic programming algorithm for POMDPs developed by Hansen that iteratively improves a finite-state controller - the previous state of the art for finite memory policies. The value of the best policy can only improve as the amount of memory increases, up to the amount needed for an optimal finite-memory policy. Our most surprising finding is that more memory helps in another way: given more memory than is needed for an optimal policy, the algorithms are more likely to converge to optimal-valued policies.
6096323
Quantifying instruction criticality for shared memory multiprocessors
Recent research on processor microarchitecture suggests using instruction criticality as a metric to guide hardware control policies. Fields et al. [3, 4] have proposed a directed acyclic graph (DAG) model for characterizing program microexecutions on uniprocessors. Under such a model, critical path analysis can be applied and instructions' slack values can be used to quantify instruction criticality. In this paper, we extend the uniprocessor DAG model to characterize parallel program executions on shared memory multiprocessor systems. We describe how critical path analysis can be applied, at a fine grain, in a multiprocessor system running both finite and continuous workloads. We provide detailed evaluations for various aspects of multiprocessor executions under the DAG model. To enable efficient offline critical path analysis, we propose a novel graph reduction technique that reduces a DAG to an equivalent but significantly smaller DAG.
21969531
Evaluation of an efficient compensation method for quantitative fan-beam brain SPECT reconstruction
Fan-beam collimators are designed to improve the system sensitivity and resolution for imaging small objects such as the human brain and breasts in single photon emission computed tomography (SPECT). Many reconstruction algorithms have been studied and applied to this geometry to deal with every kind of degradation factor. This work presents a new reconstruction approach for SPECT with circular orbit, which demonstrated good performance in terms of both accuracy and efficiency. The new approach compensates for degradation factors including noise, scatter, attenuation, and spatially variant detector response. Its uniform attenuation approximation strategy avoids the additional transmission scan for the brain attenuation map, hence reducing the patient radiation dose and furthermore simplifying the imaging procedure. We evaluate and compare this new approach with the well-established ordered-subset expectation-maximization iterative method, using Monte Carlo simulations. We perform quantitative analysis with regional bias-variance, receiver operating characteristics, and Hotelling trace studies for both methods. The results demonstrate that our reconstruction strategy has comparable performance with a significant reduction of computing time.
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.
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