Constrained Clustering

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Constrained Clustering

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Author : Sugato Basu
language : en
Publisher: CRC Press
Release Date : 2008-08-18

Constrained Clustering written by Sugato Basu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-08-18 with Business & Economics categories.

This volume encompasses many new types of constraints and clustering methods as well as delivers thorough coverage of the capabilities and limitations of constrained clustering. With contributions from industrial researchers and leading academic experts who pioneered the field, it provides a well-balanced combination of theoretical advances, key algorithmic development, and novel applications. The book presents various types of constraints for clustering and describes useful variations of the standard problem of clustering under constraints. It also demonstrates the application of clustering with constraints to relational, bibliographic, and video data.

Constrained Clustering By Constraint Programming

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Author : Khanh-Chuong Duong
language : en
Publisher:
Release Date : 2014

Constrained Clustering By Constraint Programming written by Khanh-Chuong Duong and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.

Cluster analysis is an important task in Data Mining with hundreds of different approaches in the literature. Since the last decade, the cluster analysis has been extended to constrained clustering, also called semi-supervised clustering, so as to integrate previous knowledge on data to clustering algorithms. In this dissertation, we explore Constraint Programming (CP) for solving the task of constrained clustering. The main principles in CP are: (1) users specify declaratively the problem in a Constraint Satisfaction Problem; (2) solvers search for solutions by constraint propagation and search. Relying on CP has two main advantages: the declarativity, which enables to easily add new constraints and the ability to find an optimal solution satisfying all the constraints (when there exists one). We propose two models based on CP to address constrained clustering tasks. The models are flexible and general and supports instance-level constraints and different cluster-level constraints. It also allows the users to choose among different optimization criteria. In order to improve the efficiency, different aspects have been studied in the dissertation. Experiments on various classical datasets show that our models are competitive with other exact approaches. We show that our models can easily be embedded in a more general process and we illustrate this on the problem of finding the Pareto front of a bi-criterion optimization process.

Text Clustering Models Algorithms And Applications

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Author : Zuobing Xu
language : en
Publisher:
Release Date : 2008

Text Clustering Models Algorithms And Applications written by Zuobing Xu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.

Joint Constrained Clustering And Feature Learning Based On Deep Neural Networks

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Author : Xiaoyu Liu
language : en
Publisher:
Release Date : 2017

Joint Constrained Clustering And Feature Learning Based On Deep Neural Networks written by Xiaoyu Liu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.

We propose a novel method to iteratively improve the performance of constrained clustering and feature learning based on Convolutional Neural Networks (CNNs). There is no effective strategy for neither the constraint selection nor the distance metric learning in traditional constrained clustering methods. In our work, we design an effective constraint selection strategy and combine a CNN-based feature learning approach with the constrained clustering algorithm. The proposed model consists of two iterative steps: First, we replace the random constraint selection strategy with a carefully designed one; based on the clustering result and constraints obtained, we fine tune the CNN and extract new features for distance re-calculation. Our model is evaluated on a realistic video dataset, and the experimental results demonstrate that our method can improve the constrained clustering performance and feature divisibility simultaneously even with fewer constraints.

Proceedings Of The Siam International Conference On Data Mining

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Author :
language : en
Publisher:
Release Date : 2005

Proceedings Of The Siam International Conference On Data Mining written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Data mining categories.

Constraints As Features

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Author : Shmuel Asafi
language : en
Publisher:
Release Date : 2012

Constraints As Features written by Shmuel Asafi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.

Ieee International Symposium On Information Theory 1991

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Author : IEEE Information Theory Society
language : en
Publisher: Institute of Electrical & Electronics Engineers(IEEE)
Release Date : 1991

Ieee International Symposium On Information Theory 1991 written by IEEE Information Theory Society and has been published by Institute of Electrical & Electronics Engineers(IEEE) this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Computers categories.

Proceedings Ieee International Symposium On Information Theory

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Author :
language : en
Publisher:
Release Date : 1991

Proceedings Ieee International Symposium On Information Theory written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Information theory categories.

Computational Materials Science

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Author : Feng Xiong
language : en
Publisher: Trans Tech Publications Ltd
Release Date : 2011-07-04

Computational Materials Science written by Feng Xiong and has been published by Trans Tech Publications Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-07-04 with Technology & Engineering categories.

Selected, peer reviewed papers from the 2011 International Conference on Computational Materials Science (CMS 2011) in April 17-18, Guangzhou, China

Advances In Constrained Clustering

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Author : Zijie Qi
language : en
Publisher:
Release Date : 2011

Advances In Constrained Clustering written by Zijie Qi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.

Traditional clustering techniques have been very successful for finding insights in data structures by grouping data objects into clusters based on their similarities. Without any guidance or supervision, it is likely that clustering algorithms will converge and reach an incomprehensible or useless clustering result. In this dissertation, we focus on the advances in constrained clustering which aim to improve clustering quality by making use of domain knowledge embedded in constraints.In most cases, data analysis is an iterative process; when an existing clustering is given, we might have a general judgement about whether the overall clustering is useful and wish to find another interpretation. Given an existing clustering, we explored a general purpose approach in order to find an alternative clustering. Furthermore, users may have some specific feedback on the clusters they want to keep, and thus we proposed a new and flexible method which allows the user to formally specify positive and negative feedback based on the existing clustering, ranging from which clusters to keep (or not) to making a trade-off between alternativeness and clustering quality. The next topic focused on making use of three sources of information (attribute, link, and domain) such as in social networks and documents with citations. We proposed a novel method for performing constrained clustering on directed graphs which combined all types of sources. In addition, we proposed a constraint propagation method to further enhance the results. Finally, it is well known that adding domain information can improve the quality of a clustering. However, it can be expensive to acquire such domain knowledge. Consequently, we need to limit the number of constraints we acquire and, more importantly, maximize the effectiveness of these limited numbers of constraints. We defined the problem of identifying the most informative constraints and we proposed an algorithm which generates the most informative constraints by maximizing the information gain from the constraints.