Algorithms For Clustering Problems

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Algorithms For Clustering Problems

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Author : Moses Samson Charikar
language : en
Publisher:
Release Date : 2000

Algorithms For Clustering Problems written by Moses Samson Charikar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with categories.

Data Clustering

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Author : Charu C. Aggarwal
language : en
Publisher: CRC Press
Release Date : 2016-03-29

Data Clustering written by Charu C. Aggarwal and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-29 with Business & Economics categories.

Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains. The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process—including how to verify the quality of the underlying clusters—through supervision, human intervention, or the automated generation of alternative clusters.

Algorithms For Clustering Problems

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Author : Ashkan Norouzi Fard
language : en
Publisher:
Release Date : 2018

Algorithms For Clustering Problems written by Ashkan Norouzi Fard and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.

Mots-clés de l'auteur: Linear Programming ; Approximation Algorithms ; Clustering ; Facility Location Problem ; k-Median ; k-Means.

Algorithms For Clustering Data

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Author : Anil K. Jain
language : en
Publisher:
Release Date : 1988

Algorithms For Clustering Data written by Anil K. Jain and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988 with Computers categories.

Partitional Clustering Algorithms

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Author : M. Emre Celebi
language : en
Publisher: Springer
Release Date : 2014-11-07

Partitional Clustering Algorithms written by M. Emre Celebi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-07 with Technology & Engineering categories.

This book focuses on partitional clustering algorithms, which are commonly used in engineering and computer scientific applications. The goal of this volume is to summarize the state-of-the-art in partitional clustering. The book includes such topics as center-based clustering, competitive learning clustering and density-based clustering. Each chapter is contributed by a leading expert in the field.

Approximation Algorithms For Clustering Problems

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Author : Babak Behsaz
language : en
Publisher:
Release Date : 2012

Approximation Algorithms For Clustering Problems written by Babak Behsaz and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Approximation theory categories.

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.

Approximation Algorithms For Clustering And Facility Location Problems

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Author : Sara Ahmadian
language : en
Publisher:
Release Date : 2017

Approximation Algorithms For Clustering And Facility Location Problems written by Sara Ahmadian and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Approximation theory categories.

Facility location problems arise in a wide range of applications such as plant or warehouse location problems, cache placement problems, and network design problems, and have been widely studied in Computer Science and Operations Research literature. These problems typically involve an underlying set F of facilities that provide service, and an underlying set D of clients that require service, which need to be assigned to facilities in a cost-effective fashion. This abstraction is quite versatile and also captures clustering problems, where one typically seeks to partition a set of data points into k clusters, for some given k, in a suitable way, which themselves find applications in data mining, machine learning, and bioinformatics. Basic variants of facility location problems are now relatively well-understood, but we have much-less understanding of more-sophisticated models that better model the real-world concerns. In this thesis, we focus on three models inspired by some real-world optimization scenarios. In Chapter 2, we consider mobile facility location (MFL) problem, wherein we seek to relocate a given set of facilities to destinations closer to the clients as to minimize the sum of facility-movement and client-assignment costs. This abstracts facility-location settings where one has the flexibility of moving facilities from their current locations to other destinations so as to serve clients more efficiently by reducing their assignment costs. We give the first local-search based approximation algorithm for this problem and achieve the best-known approximation guarantee. Our main result is (3+epsilon)-approximation for this problem for any constant epsilon > 0 using local search which improves the previous best guarantee of 8-approximation algorithm due to [34] based on LP-rounding. Our results extend to the weighted generalization wherein each facility i has a non-negative weight w_i and the movement cost for i is w_i times the distance traveled by i. In Chapter 3, we consider a facility-location problem that we call the minimum-load k-facility location (MLkFL), which abstracts settings where the cost of serving the clients assigned to a facility is incurred by the facility. This problem was studied under the name of min-max star cover in [32,10], who (among other results) gave bicriteria approximation algorithms for MLkFL when F=D. MLkFL is rather poorly understood, and only an O(k)-approximation is currently known for MLkFL, even for line metrics. Our main result is the first polytime approximation scheme (PTAS) for MLkFL on line metrics (note that no non-trivial true approximation of any kind was known for this metric). Complementing this, we prove that MLkFL is strongly NP-hard on line metrics. In Chapter 4, we consider clustering problems with non-uniform lower bounds and outliers, and obtain the first approximation guarantees for these problems. We consider objective functions involving the radii of open facilities, where the radius of a facility i is the maximum distance between i and a client assigned to it. We consider two problems: minimizing the sum of the radii of the open facilities, which yields the lower-bounded min-sum-of-radii with outliers (LBkSRO) problem, and minimizing the maximum radius, which yields the lower-bounded k-supplier with outliers (LBkSupO) problem. We obtain an approximation factor of 12.365 for LBkSRO, which improves to 3.83 for the non-outlier version. These also constitute the first approximation bounds for the min-sum-of-radii objective when we consider lower bounds and outliers separately. We obtain approximation factors of 5 and 3 respectively for LBkSupO and its non-outlier version. These are the first approximation results for k-supplier with non-uniform lower bounds.

Partitional Clustering Via Nonsmooth Optimization

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Author : Adil Bagirov
language : en
Publisher: Springer Nature
Release Date : 2024-12-16

Partitional Clustering Via Nonsmooth Optimization written by Adil Bagirov and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-16 with Technology & Engineering categories.

This updated book describes optimization models of clustering problems and clustering algorithms based on optimization techniques, including their implementation, evaluation, and applications. The book gives a comprehensive and detailed description of optimization approaches for solving clustering problems; the authors' emphasis on clustering algorithms is based on deterministic methods of optimization. The book also includes results on real-time clustering algorithms based on optimization techniques, addresses implementation issues of these clustering algorithms, and discusses new challenges arising from very large data and data with noise and outliers. The book is ideal for anyone teaching or learning clustering algorithms. It provides an accessible introduction to the field and it is well suited for practitioners already familiar with the basics of optimization.

Problem Solving In Algorithms A Research Approach

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Author : Sanpawat Kantabutra
language : en
Publisher: ศูนย์บริหารงานวิจัย สำนักงานมหาวิทยาลัยเชียงใหม่
Release Date : 2021-03-01

Problem Solving In Algorithms A Research Approach written by Sanpawat Kantabutra and has been published by ศูนย์บริหารงานวิจัย สำนักงานมหาวิทยาลัยเชียงใหม่ this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-01 with Computers categories.

This is THE book for every serious researcher in theoretical computer science. The book exposes critical detail in problem solving and researching in the fields of algorithms and complexity that no other book has ever done. It reveals the secrets of doing research and the way of thinking that are so natural to the world’s top computer scientists. Such skills and thinking are so “second nature” to every top computer scientist that they are not even mentioned or talked about. This book is thus for everyone who seriously wants to become an excellent researcher but may not have such skills and thinking.