Results 1  10
of
19
Survey of clustering algorithms
 IEEE TRANSACTIONS ON NEURAL NETWORKS
, 2005
"... Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the ..."
Abstract

Cited by 231 (3 self)
 Add to MetaCart
Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.
Revisiting T. Uno and M. Yagiura’s algorithm
 Proc. 16th International Symposium on Algorithms and Computation, in Lecture Notes in Comput. Sci
, 2005
"... Abstract. In 2000, T. Uno and M. Yagiura published an algorithm that computes all the K common intervals of two given permutations of length n in O(n + K) time. Our paper first presents a decomposition approach to obtain a compact encoding for common intervals of d permutations. Then, we revisit T. ..."
Abstract

Cited by 20 (6 self)
 Add to MetaCart
Abstract. In 2000, T. Uno and M. Yagiura published an algorithm that computes all the K common intervals of two given permutations of length n in O(n + K) time. Our paper first presents a decomposition approach to obtain a compact encoding for common intervals of d permutations. Then, we revisit T. Uno and M. Yagiura’s algorithm to yield a linear time algorithm for finding this encoding. Besides, we adapt the algorithm to obtain a linear time modular decomposition of an undirected graph, and thereby propose a formal invariantbased proof for all these algorithms. 1
C.: Drawing graphs using modular decomposition
 Graph Drawing. Volume LNCS 3843
, 2005
"... In this paper we present an algorithm for drawing an undirected graph G that takes advantage of the structure of the modular decomposition tree of G. Specifically, our algorithm works by traversing the modular decomposition tree of the input graph G on n vertices and m edges in a bottomup fashion u ..."
Abstract

Cited by 7 (1 self)
 Add to MetaCart
In this paper we present an algorithm for drawing an undirected graph G that takes advantage of the structure of the modular decomposition tree of G. Specifically, our algorithm works by traversing the modular decomposition tree of the input graph G on n vertices and m edges in a bottomup fashion until it reaches the root of the tree, while at the same time intermediate drawings are computed. In order to achieve aesthetically pleasing results, we use grid and circular placement techniques, and utilize an appropriate modification of a wellknown spring embedder algorithm. It turns out, that for some classes of graphs, our algorithm runs in O(n + m) time, while in general, the running time is bounded in terms of the processing time of the spring embedder algorithm. The result is a drawing that reveals the structure of the graph G and preserves certain aesthetic criteria.
Algebraic Operations on PQ Trees and Modular Decomposition Trees
, 2005
"... Partitive set families are families of sets that can be quite large, but have a compact, recursive representation in the form of a tree. This tree is a common generalization of PQ trees, the modular decomposition of graphs, certain decompositions of boolean functions, and decompositions that arise o ..."
Abstract

Cited by 6 (1 self)
 Add to MetaCart
Partitive set families are families of sets that can be quite large, but have a compact, recursive representation in the form of a tree. This tree is a common generalization of PQ trees, the modular decomposition of graphs, certain decompositions of boolean functions, and decompositions that arise on a variety of other combinatorial structures. We describe natural operators on partitive set families, give algebraic identities for manipulating them, and describe efficient algorithms for evaluating them. We use these results to obtain new time bounds for finding the common intervals of a set of permutations, finding the modular decomposition of an edgecolored graphs (also known as a twostructure), finding the PQ tree of a matrix when a consecutiveones arrangement is given, and finding the modular decomposition of a permutation graph when its permutation realizer is given.
Homogeneity vs. adjacency: generalising some graph decomposition algorithms
 In 32nd International Workshop on GraphTheoretic Concepts in Computer Science (WG), volume 4271 of LNCS
, 2006
"... Abstract. In this paper, a new general decomposition theory inspired from modular graph decomposition is presented. Our main result shows that, within this general theory, most of the nice algorithmic tools developed for modular decomposition are still efficient. This theory not only unifies the usu ..."
Abstract

Cited by 6 (2 self)
 Add to MetaCart
Abstract. In this paper, a new general decomposition theory inspired from modular graph decomposition is presented. Our main result shows that, within this general theory, most of the nice algorithmic tools developed for modular decomposition are still efficient. This theory not only unifies the usual modular decomposition generalisations such as modular decomposition of directed graphs and of 2structures, but also decomposition by star cutsets. 1
Dynamic Distance Hereditary Graphs Using Split Decomposition
, 2007
"... The problem of maintaining a representation of a dynamic graph as long as a certain property is satisfied, has recently been considered for a number of properties. This paper presents an optimal algorithm for this problem on vertexdynamic connected distance hereditary graphs: both vertex insertion ..."
Abstract

Cited by 3 (1 self)
 Add to MetaCart
The problem of maintaining a representation of a dynamic graph as long as a certain property is satisfied, has recently been considered for a number of properties. This paper presents an optimal algorithm for this problem on vertexdynamic connected distance hereditary graphs: both vertex insertion and deletion have complexity O(d), where d is the degree of the vertex involved in the modification. Our vertexdynamic algorithm is competitive with the existing linear time recognition algorithms of distance hereditary graphs, and is also simpler. To achieve this, we revisit the split decomposition by which distance hereditary graphs are known to be completely decomposable. We propose a formulation of this decomposition in terms of graphlabelled trees. Doing so, we are also able to derive an intersection model for distance hereditary graphs, which answers an open problem.
A Note On Computing Set Overlap Classes
, 711
"... Abstract. Let V be a finite set of n elements and F = {X1, X2,..., Xm} a family of m subsets of V. Two sets Xi and Xj of F overlap if Xi ∩Xj �= ∅, Xj \ Xi � = ∅, and Xi \ Xj � = ∅. Two sets X, Y ∈ F are in the same overlap class if there is a series X = X1, X2,..., Xk = Y of sets of F in which each ..."
Abstract

Cited by 3 (2 self)
 Add to MetaCart
Abstract. Let V be a finite set of n elements and F = {X1, X2,..., Xm} a family of m subsets of V. Two sets Xi and Xj of F overlap if Xi ∩Xj �= ∅, Xj \ Xi � = ∅, and Xi \ Xj � = ∅. Two sets X, Y ∈ F are in the same overlap class if there is a series X = X1, X2,..., Xk = Y of sets of F in which each XiXi+1 overlaps. In this note, we focus on efficiently identifying all overlap classes in O(n + � m i=1 Xi) time. We thus revisit the clever algorithm of Dahlhaus [2] of which we give a clear presentation and that we simplify to make it practical and implementable in its real worst case complexity. An useful variant of Dahlhaus’s approach is also explained. 1
Coloring a Graph Using Split Decomposition
"... Abstract. We show how to use split decomposition to compute the weighted clique number and the chromatic number of a graph and we apply these results to some classes of graphs. In particular we present an O(n 2 m) algorithm to compute the chromatic number for all those graphs having a split decompos ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
Abstract. We show how to use split decomposition to compute the weighted clique number and the chromatic number of a graph and we apply these results to some classes of graphs. In particular we present an O(n 2 m) algorithm to compute the chromatic number for all those graphs having a split decomposition in which every prime graph is an induced subgraph of either a Ck or a Ck for some k ≥ 3. 1
A Survey On: Content Based Image Retrieval Systems Using Clustering Techniques For Large Data sets
"... Contentbased image retrieval (CBIR) is a new but widely adopted method for finding images from vast and unannotated image databases. As the network and development of multimedia technologies are becoming more popular, users are not satisfied with the traditional information retrieval techniques. So ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
Contentbased image retrieval (CBIR) is a new but widely adopted method for finding images from vast and unannotated image databases. As the network and development of multimedia technologies are becoming more popular, users are not satisfied with the traditional information retrieval techniques. So nowadays the content based image retrieval (CBIR) are becoming a source of exact and fast retrieval. In recent years, a variety of techniques have been developed to improve the performance of CBIR. Data clustering is an unsupervised method for extraction hidden pattern from huge data sets. With large data sets, there is possibility of high dimensionality. Having both accuracy and efficiency for high dimensional data sets with enormous number of samples is a challenging arena. In this paper the clustering techniques are discussed and analysed. Also, we propose a method HDK that uses more than one clustering technique to improve the performance of CBIR.This method makes use of hierachical and divide and conquer KMeans clustering technique with equivalency and compatible relation concepts to improve the performance of the KMeans for using in high dimensional datasets. It also introduced the feature like color, texture and shape for accurate and effective retrieval system.
Social Networking for Scientists Using Tagging and Shared Bookmarks: a Web 2.0 Application
"... Webbased social networks, online personal profiles, keyword tagging, and online bookmarking are staples of Web 2.0style applications. In this paper we report our investigation and implementation of these capabilities as a means for creating communities of likeminded faculty and researchers, parti ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
Webbased social networks, online personal profiles, keyword tagging, and online bookmarking are staples of Web 2.0style applications. In this paper we report our investigation and implementation of these capabilities as a means for creating communities of likeminded faculty and researchers, particularly at minority serving institutions. Our motivating problem is to provide outreach tools that broaden the participation of these groups in funded research activities, particularly in cyberinfrastructure and eScience. In this paper, we discuss the system design, implementation, social network seeding, and portal capabilities. Underlying our system, and folksonomy systems generally, is a graphbased data model that links external URLs, system users, and descriptive tags. We conclude with a survey of the applicability of clustering and other data mining techniques to these folksonomy graphs. and researchers to find both useful online resources and also potential collaborators on future research projects. We are particularly interested in helping researchers at Minority Serving Institutions (MSIs) connect with each other and with the education, outreach, and training services that are designed to serve them, expanding their participation in cyberinfrastructure research efforts. This portal is a development activity of the Minority Serving InstitutionCyberinfrastructure Empowerment Coalition (MSICIEC). The portal’s home page view is shown in Figure 1. The MSICIEC social networking Web portal combines social bookmarking and tagging with online curricula vitae profiles. The display shows the loggedin user’s tag cloud (“My Tags ” on left), taggable RSS feeds (center), and tag clouds of all users (“Favorite Tags ” and “Recent Tags ” on the right). Users may search tags (including researcher names, NSF directorates, and TeraGrid allocations) using the center text field.