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Titlebook: Unsupervised Learning Algorithms; M. Emre Celebi,Kemal Aydin Book 2016 Springer International Publishing Switzerland 2016 Big Data Pattern

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发表于 2025-3-21 19:43:41 | 显示全部楼层 |阅读模式
书目名称Unsupervised Learning Algorithms
编辑M. Emre Celebi,Kemal Aydin
视频video
概述Contains the state-of-the-art in unsupervised learning in a single comprehensive volume.Features numerous step-by-step tutorials help the reader to learn quickly
图书封面Titlebook: Unsupervised Learning Algorithms;  M. Emre Celebi,Kemal Aydin Book 2016 Springer International Publishing Switzerland 2016 Big Data Pattern
描述.This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how with the proliferation of massive amounts of unlabeled data, unsupervised learning algorithms, which can automatically discover interesting and useful patterns in such data, have gained popularity among researchers and practitioners. The authors outline how these algorithms have found numerous applications including pattern recognition, market basket analysis, web mining, social network analysis, information retrieval, recommender systems, market research, intrusion detection, and fraud detection. They present how the difficulty of developing theoretically sound approaches that are amenable to objective evaluation have resulted in the proposal of numerous unsupervised learning algorithms over the past half-century. The intended audience includes researchers and practitioners who are increasingly using unsupervised learning algorithms to analyze their data. Topics of interest includeanomaly detection, clustering, feature extraction, and applications of unsupervised learning. Each chapter is contributed by a leading expert in the field..
出版日期Book 2016
关键词Big Data Patterns; Data Analytics; Data Mining; Elements Statistical Learning; Genomic Data Sets; Machine
版次1
doihttps://doi.org/10.1007/978-3-319-24211-8
isbn_softcover978-3-319-79590-4
isbn_ebook978-3-319-24211-8
copyrightSpringer International Publishing Switzerland 2016
1 Front Matter
Abstract
2 ,Anomaly Detection for Data with Spatial Attributes, P. Deepak
Abstract
3 ,Anomaly Ranking in a High Dimensional Space: The Unsupervised TreeRank Algorithm, S. Clémençon,N. Baskiotis,N. Vayatis
Abstract
4 ,Genetic Algorithms for Subset Selection in Model-Based Clustering, Luca Scrucca
Abstract
5 ,Clustering Evaluation in High-Dimensional Data, Nenad Tomašev,Miloš Radovanović
Abstract
6 ,Combinatorial Optimization Approaches for Data Clustering, Paola Festa
Abstract
7 ,Kernel Spectral Clustering and Applications, Rocco Langone,Raghvendra Mall,Carlos Alzate,Johan A. K. Suykens
Abstract
8 ,Uni- and Multi-Dimensional Clustering Via Bayesian Networks, Omid Keivani,Jose M. Peña
Abstract
9 ,A Radial Basis Function Neural Network Training Mechanism for Pattern Classification Tasks, Antonios D. Niros,George E. Tsekouras
Abstract
10 ,A Survey of Constrained Clustering, Derya Dinler,Mustafa Kemal Tural
Abstract
11 ,An Overview of the Use of Clustering for Data Privacy, Vicenç Torra,Guillermo Navarro-Arribas,Klara Stokes
Abstract
12 ,Nonlinear Clustering: Methods and Applications, Chang-Dong Wang,Jian-Huang Lai
Abstract
13 ,Swarm Intelligence-Based Clustering Algorithms: A Survey, Tülin İnkaya,Sinan Kayalıgil,Nur Evin Özdemirel
Abstract
14 ,Extending Kmeans-Type Algorithms by Integrating Intra-cluster Compactness and Inter-cluster Separat Xiaohui Huang,Yunming Ye,Haijun Zhang
Abstract
15 ,A Fuzzy-Soft Competitive Learning Approach for Grayscale Image Compression, Dimitrios M. Tsolakis,George E. Tsekouras
Abstract
16 ,Unsupervised Learning in Genome Informatics, Ka-Chun Wong,Yue Li,Zhaolei Zhang
Abstract
17 ,The Application of LSA to the Evaluation of Questionnaire Responses, Dian I. Martin,John C. Martin,Michael W. Berry
Abstract
18 ,Mining Evolving Patterns in Dynamic Relational Networks, Rezwan Ahmed,George Karypis
Abstract
19 ,Probabilistically Grounded Unsupervised Training of Neural Networks, Edmondo Trentin,Marco Bongini
Abstract
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发表于 2025-3-21 22:47:14 | 显示全部楼层
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A Radial Basis Function Neural Network Training Mechanism for Pattern Classification Tasks,d cluster centers coincide with the centers of the network’s basis functions. The method of PSO is used to estimate the neuron connecting weights involved in the learning process. The proposed classifier is applied to three machine learning data sets, and its results are compared to other relative approaches that exist in the literature.
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Anomaly Ranking in a High Dimensional Space: The Unsupervised TreeRank Algorithm, surveillance, monitoring of complex systems/infrastructures such as energy networks or aircraft engines, system management in data centers). However, the learning aspect of unsupervised ranking has only received attention in the machine-learning community in the past few years. The Mass-Volume (MV)
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Clustering Evaluation in High-Dimensional Data,rated cluster configurations. This is especially useful for comparing the performance of different clustering algorithms as well as determining the optimal number of clusters in clustering algorithms that do not estimate it internally. Many clustering quality indexes have been proposed over the year
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Combinatorial Optimization Approaches for Data Clustering,objects belong to the same group or cluster. The greater the similarity within a cluster and the greater the dissimilarity between clusters, the better the clustering task has been performed. Starting from the 1990s, cluster analysis has emerged as an important interdisciplinary field, applied to se
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