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173
Genetic Network Inference: From CoExpression Clustering To Reverse Engineering
, 2000
"... motivation: Advances in molecular biological, analytical and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using highthroughput gene expression assays, we are able to measure the output of the ge ..."
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Cited by 335 (0 self)
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motivation: Advances in molecular biological, analytical and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using highthroughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of coexpression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiplecluster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e. who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and nonlinear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting and bioengineering.
Parallel Algorithms for Hierarchical Clustering
 Parallel Computing
, 1995
"... Hierarchical clustering is a common method used to determine clusters of similar data points in multidimensional spaces. O(n 2 ) algorithms are known for this problem [3, 4, 10, 18]. This paper reviews important results for sequential algorithms and describes previous work on parallel algorithms f ..."
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Cited by 107 (2 self)
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Hierarchical clustering is a common method used to determine clusters of similar data points in multidimensional spaces. O(n 2 ) algorithms are known for this problem [3, 4, 10, 18]. This paper reviews important results for sequential algorithms and describes previous work on parallel algorithms for hierarchical clustering. Parallel algorithms to perform hierarchical clustering using several distance metrics are then described. Optimal PRAM algorithms using n log n processors are given for the average link, complete link, centroid, median, and minimum variance metrics. Optimal butterfly and tree algorithms using n log n processors are given for the centroid, median, and minimum variance metrics. Optimal asymptotic speedups are achieved for the best practical algorithm to perform clustering using the single link metric on a n log n processor PRAM, butterfly, or tree. Keywords. Hierarchical clustering, pattern analysis, parallel algorithm, butterfly network, PRAM algorithm. 1 In...
Cluster analysis and mathematical programming
 MATHEMATICAL PROGRAMMING
, 1997
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Evaluating Document Clustering for Interactive Information Retrieval
 In Proceedings of the tenth International Conference on Information and Knowledge Managment (CIKM
, 2001
"... We consider the problem of organizing and browsing the top ranked portion of the documents returned by an information retrieval system. We study the effectiveness of a document organization in helping a user to locate the relevant material among the retrieved documents as quickly as possible. In thi ..."
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Cited by 70 (4 self)
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We consider the problem of organizing and browsing the top ranked portion of the documents returned by an information retrieval system. We study the effectiveness of a document organization in helping a user to locate the relevant material among the retrieved documents as quickly as possible. In this context we examine a set of clustering algorithms and experimentally show that a clustering of the retrieved documents can be significantly more effective than traditional ranked list approach. We also show that the clustering approach can be as effective as the interactive relevance feedback based on query expansion while retaining an important advantage  it provides the user with a valuable sense of control over the feedback process.
Using Interdocument Similarity Information in Document Retrieval Systems
 Journal of the American Society for Information Science
, 1986
"... The first part of this paper reports a comparative study of the document classifications produced by the use of the single linkage, complete linkage, group average, and Ward clustering methods. Studies of cluster membership and of the effectiveness of cluster searches support previous findings that ..."
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Cited by 59 (2 self)
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The first part of this paper reports a comparative study of the document classifications produced by the use of the single linkage, complete linkage, group average, and Ward clustering methods. Studies of cluster membership and of the effectiveness of cluster searches support previous findings that suggest that the single linkage classifications are rather different from those produced by the other three methods. These latter methods all produce large numbers of small clusters containing just pairs of documents. This finding motivates the work reported in the second part of the paper, which considers the use of clusters consisting of a document together with that document with which it is most similar. A comparison of the use of such clusters with conventional best match searches using seven document test collections suggests that the two types of search are of comparable effectiveness, but they retrieve noticeably different sets of relevant documents.
Scalesets image analysis
 International Journal of Computer Vision
, 2006
"... This paper introduces a multiscale theory of piecewise image modelling, called the scalesets theory, and which can be regarded as a regionoriented scalespace theory. The first part of the paper studies the general structure of a geometrically unbiased regionoriented multiscale image descriptio ..."
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Cited by 53 (4 self)
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This paper introduces a multiscale theory of piecewise image modelling, called the scalesets theory, and which can be regarded as a regionoriented scalespace theory. The first part of the paper studies the general structure of a geometrically unbiased regionoriented multiscale image description and introduces the scalesets representation, a representation which allows to handle such a description exactly. The second part of the paper deals with the way scalesets image analyses can be built according to an energy minimization principle. We consider a rather general formulation of the partitioning problem which involves minimizing a twotermbased energy, of the form λC+D, where D is a goodnessoffit term and C is a regularization term. We describe the way such energies arise from basic principles of approximate modelling and we relate them to operational rate/distorsion problems involved in lossy compression problems. We then show that an important subset of these energies constitutes a class of multiscale energies in that the minimal cut of a hierarchy gets coarser and coarser as parameter λ increases. This allows us to devise a fast dynamicprogramming procedure to find the complete scalesets representation of this family of minimal cuts. Considering then the construction of the hierarchy from which the minimal cuts are extracted, we end up with an exact and parameterfree
Meta clustering
 In Proceedings IEEE International Conference on Data Mining
, 2006
"... Clustering is illdefined. Unlike supervised learning where labels lead to crisp performance criteria such as accuracy and squared error, clustering quality depends on how the clusters will be used. Devising clustering criteria that capture what users need is difficult. Most clustering algorithms se ..."
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Cited by 41 (1 self)
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Clustering is illdefined. Unlike supervised learning where labels lead to crisp performance criteria such as accuracy and squared error, clustering quality depends on how the clusters will be used. Devising clustering criteria that capture what users need is difficult. Most clustering algorithms search for optimal clusterings based on a prespecified clustering criterion. Our approach differs. We search for many alternate clusterings of the data, and then allow users to select the clustering(s) that best fit their needs. Meta clustering first finds a variety of clusterings and then clusters this diverse set of clusterings so that users must only examine a small number of qualitatively different clusterings. We present methods for automatically generating a diverse set of alternate clusterings, as well as methods for grouping clusterings into meta clusters. We evaluate meta clustering on four test problems and two case studies. Surprisingly, clusterings that would be of most interest to users often are not very compact clusterings. 1.
Identifying Community Structures from Network Data via Maximum Likelihood Methods
, 2005
"... In many economic situations it is of interest to know who interacts with whom. In international trade, for example, some theories predict that members of certaing groups will have a higher probability of trading with each other than with those in other groups. Based on a model of within and across g ..."
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Cited by 40 (10 self)
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In many economic situations it is of interest to know who interacts with whom. In international trade, for example, some theories predict that members of certaing groups will have a higher probability of trading with each other than with those in other groups. Based on a model of within and across group interactions, we describe, characterize, and implement, a new method for identifying trading or community structures from network data. The method is based on maximum likelihood estimation, a standard statistical tool.
CrimeNet explorer: a framework for criminal network knowledge discovery
 ACM Transactions on Information Systems (TOIS
, 2005
"... Knowledge about the structure and organization of criminal networks is important for both crime investigation and the development of effective strategies to prevent crimes. However, except for network visualization, criminal network analysis remains primarily a manual process. Existing tools do not ..."
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Cited by 40 (7 self)
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Knowledge about the structure and organization of criminal networks is important for both crime investigation and the development of effective strategies to prevent crimes. However, except for network visualization, criminal network analysis remains primarily a manual process. Existing tools do not provide advanced structural analysis techniques that allow extraction of network knowledge from large volumes of criminaljustice data. To help law enforcement and intelligence agencies discover criminal network knowledge efficiently and effectively, in this research we proposed a framework for automated network analysis and visualization. The framework included four stages: network creation, network partition, structural analysis, and network visualization. Based upon it, we have developed a system called CrimeNet Explorer that incorporates several advanced techniques: a concept space approach, hierarchical clustering, social network analysis methods, and multidimensional scaling. Results from controlled experiments involving student subjects demonstrated that our system could achieve higher clustering recall and precision than did untrained subjects when detecting subgroups from criminal networks. Moreover, subjects identified central members and interaction patterns between groups significantly faster with the help of structural analysis functionality than with only visualization functionality. No significant gain in
Efficient clustering and matching for object class recognition
 In Proc. BMVC
, 2006
"... In this paper we address the problem of building object class representations based on local features and fast matching in a large database. We propose an efficient algorithm for hierarchical agglomerative clustering. We examine different agglomerative and partitional clustering strategies and compa ..."
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Cited by 35 (4 self)
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In this paper we address the problem of building object class representations based on local features and fast matching in a large database. We propose an efficient algorithm for hierarchical agglomerative clustering. We examine different agglomerative and partitional clustering strategies and compare the quality of obtained clusters. Our combination of partitionalagglomerative clustering gives significant improvement in terms of efficiency while maintaining the same quality of clusters. We also propose a method for building data structures for fast matching in high dimensional feature spaces. These improvements allow to deal with large sets of training data typically used in recognition of multiple object classes. 1