Results 1  10
of
66
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 ..."
Abstract

Cited by 210 (0 self)
 Add to MetaCart
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 ..."
Abstract

Cited by 80 (1 self)
 Add to MetaCart
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...
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 ..."
Abstract

Cited by 56 (4 self)
 Add to MetaCart
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.
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 ..."
Abstract

Cited by 33 (3 self)
 Add to MetaCart
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
Similarity Pyramids for Browsing and Organization of Large Image Databases
, 1998
"... The advent of large image databases (>10,000) has created a need for tools which can search and organize images automatically by their content. This paper presents a method for designing a hierarchical browsing environment which we call a similarity pyramid. The similarity pyramid groups similar ima ..."
Abstract

Cited by 29 (7 self)
 Add to MetaCart
The advent of large image databases (>10,000) has created a need for tools which can search and organize images automatically by their content. This paper presents a method for designing a hierarchical browsing environment which we call a similarity pyramid. The similarity pyramid groups similar images together while allowing users to view the database at varying levels of resolution. We show that the similarity pyramid is best constructed using agglomerative (bottomup) clustering methods, and present a fastsparse clustering method which dramatically reduces both memory and computation over conventional methods. We then present an objective measure of pyramid organization called dispersion, and we use it to show that our fastsparse clustering method produces better similarity pyramids than top down approaches.
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 ..."
Abstract

Cited by 27 (4 self)
 Add to MetaCart
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
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 ..."
Abstract

Cited by 24 (9 self)
 Add to MetaCart
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.
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 ..."
Abstract

Cited by 22 (1 self)
 Add to MetaCart
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.
An interior point algorithm for minimum sum of squares clustering
 SIAM J. Sci. Comput
, 1997
"... Abstract. An exact algorithm is proposed for minimum sumofsquares nonhierarchical clustering, i.e., for partitioning a given set of points from a Euclidean mspace into a given number of clusters in order to minimize the sum of squared distances from all points to the centroid of the cluster to wh ..."
Abstract

Cited by 21 (8 self)
 Add to MetaCart
Abstract. An exact algorithm is proposed for minimum sumofsquares nonhierarchical clustering, i.e., for partitioning a given set of points from a Euclidean mspace into a given number of clusters in order to minimize the sum of squared distances from all points to the centroid of the cluster to which they belong. This problem is expressed as a constrained hyperbolic program in 01 variables. The resolution method combines an interior point algorithm, i.e., a weighted analytic center column generation method, with branchandbound. The auxiliary problem of determining the entering column (i.e., the oracle) is an unconstrained hyperbolic program in 01 variables with a quadratic numerator and linear denominator. It is solved through a sequence of unconstrained quadratic programs in 01 variables. To accelerate resolution, variable neighborhood search heuristics are used both to get a good initial solution and to solve quickly the auxiliary problem as long as global optimality is not reached. Estimated bounds for the dual variables are deduced from the heuristic solution and used in the resolution process as a trust region. Proved minimum sumofsquares partitions are determined for the first time for several fairly large data sets from the literature, including Fisher’s 150 iris. Key words. classification and discrimination, cluster analysis, interiorpoint methods, combinatorial optimization
ViBE: A Video Indexing and Browsing Environment
 CERIAS TECH REPORT 2001109
"... In this paper, we describe a unique new paradigm for video database management known as ViBE (Video Indexing and Browsing Environment). ViBE is a browseable/searchable paradigm for organizing video data containing a large number of sequences. We describe how ViBE performs on a database of MPEG seque ..."
Abstract

Cited by 17 (4 self)
 Add to MetaCart
In this paper, we describe a unique new paradigm for video database management known as ViBE (Video Indexing and Browsing Environment). ViBE is a browseable/searchable paradigm for organizing video data containing a large number of sequences. We describe how ViBE performs on a database of MPEG sequences.