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Clustering with Relative Constraints

by Eric Yi Liu, Zhaojun Zhang, Wei Wang
"... Recent studies [26, 22] have suggested using relative distance comparisons as constraints to represent domain knowledge. A natural extension to relative comparisons is the combination of two comparisons defined on the same set of three instances. Constraints in this form, termed Relative Constraints ..."
Abstract - Cited by 3 (1 self) - Add to MetaCart
Constraints, provide a unified knowledge representation for both partitional and hierarchical clusterings. But many key properties of relative constraints remain unknown. In this paper, we answer the following important questions that enable the broader application of relative constraints in general

Distributional Clustering Of English Words

by Fernando Pereira, Naftali Tishby, Lillian Lee - In Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics , 1993
"... We describe and evaluate experimentally a method for clustering words according to their dis- tribution in particular syntactic contexts. Words are represented by the relative frequency distributions of contexts in which they appear, and relative entropy between those distributions is used as the si ..."
Abstract - Cited by 629 (27 self) - Add to MetaCart
We describe and evaluate experimentally a method for clustering words according to their dis- tribution in particular syntactic contexts. Words are represented by the relative frequency distributions of contexts in which they appear, and relative entropy between those distributions is used

Constraint Logic Programming: A Survey

by Joxan Jaffar, Michael J. Maher
"... Constraint Logic Programming (CLP) is a merger of two declarative paradigms: constraint solving and logic programming. Although a relatively new field, CLP has progressed in several quite different directions. In particular, the early fundamental concepts have been adapted to better serve in differe ..."
Abstract - Cited by 869 (25 self) - Add to MetaCart
Constraint Logic Programming (CLP) is a merger of two declarative paradigms: constraint solving and logic programming. Although a relatively new field, CLP has progressed in several quite different directions. In particular, the early fundamental concepts have been adapted to better serve

Survey of clustering algorithms

by Rui Xu, Donald Wunsch II - 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 499 (4 self) - Add to MetaCart
. Several tightly related topics, proximity measure, and cluster validation, are also discussed.

Knowledge acquisition via incremental conceptual clustering

by Douglas H. Fisher - Machine Learning , 1987
"... hill climbing Abstract. Conceptual clustering is an important way of summarizing and explaining data. However, the recent formulation of this paradigm has allowed little exploration of conceptual clustering as a means of improving performance. Furthermore, previous work in conceptual clustering has ..."
Abstract - Cited by 765 (9 self) - Add to MetaCart
not explicitly dealt with constraints imposed by real world environments. This article presents COBWEB, a conceptual clustering system that organizes data so as to maximize inference ability. Additionally, COBWEB is incremental and computationally economical, and thus can be flexibly applied in a variety

Model-Based Clustering, Discriminant Analysis, and Density Estimation

by Chris Fraley, Adrian E. Raftery - JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION , 2000
"... Cluster analysis is the automated search for groups of related observations in a data set. Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures and most clustering methods available in commercial software are also of this type. However, there is little ..."
Abstract - Cited by 573 (29 self) - Add to MetaCart
Cluster analysis is the automated search for groups of related observations in a data set. Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures and most clustering methods available in commercial software are also of this type. However

Partial Constraint Satisfaction

by Eugene C. Freuder, Richard J. Wallace , 1992
"... . A constraint satisfaction problem involves finding values for variables subject to constraints on which combinations of values are allowed. In some cases it may be impossible or impractical to solve these problems completely. We may seek to partially solve the problem, in particular by satisfying ..."
Abstract - Cited by 471 (21 self) - Add to MetaCart
satisfaction problems illuminates the relative and absolute effectiveness of these methods. A general model of partial constraint satisfaction is proposed. 1 Introduction Constraint satisfaction involves finding values for problem variables subject to constraints on acceptable combinations of values

BIRCH: an efficient data clustering method for very large databases

by Tian Zhang, Raghu Ramakrishnan, Miron Livny - In Proc. of the ACM SIGMOD Intl. Conference on Management of Data (SIGMOD , 1996
"... Finding useful patterns in large datasets has attracted considerable interest recently, and one of the most widely st,udied problems in this area is the identification of clusters, or deusel y populated regions, in a multi-dir nensional clataset. Prior work does not adequately address the problem of ..."
Abstract - Cited by 576 (2 self) - Add to MetaCart
multi-dimensional metric data points to try to produce the best quality clustering with the available resources (i. e., available memory and time constraints). BIRCH can typically find a goocl clustering with a single scan of the data, and improve the quality further with a few aclditioual scans. BIRCH

Constrained K-means Clustering with Background Knowledge

by Kiri Wagstaff, Claire Cardie, Seth Rogers, Stefan Schroedl - In ICML , 2001
"... Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. In this paper, we demonstrate how the popular k-means clustering algorithm can be pro tably modi- ed ..."
Abstract - Cited by 488 (9 self) - Add to MetaCart
to make use of this information. In experiments with arti cial constraints on six data sets, we observe improvements in clustering accuracy. We also apply this method to the real-world problem of automatically detecting road lanes from GPS data and observe dramatic increases in performance. 1.

Numerical integration of the Cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes

by Jean-paul Ryckaert, Giovanni Ciccotti, Herman J. C. Berendsen - J. Comput. Phys , 1977
"... A numerical algorithm integrating the 3N Cartesian equations of motion of a system of N points subject to holonomic constraints is formulated. The relations of constraint remain perfectly fulfilled at each step of the trajectory despite the approximate character of numerical integration. The method ..."
Abstract - Cited by 704 (6 self) - Add to MetaCart
A numerical algorithm integrating the 3N Cartesian equations of motion of a system of N points subject to holonomic constraints is formulated. The relations of constraint remain perfectly fulfilled at each step of the trajectory despite the approximate character of numerical integration. The method
Results 1 - 10 of 52,189