Advances in Fuzzy Clustering and its Applications
暫譯: 模糊聚類技術的進展及其應用
Jose Valente de Oliveira, Witold Pedrycz
- 出版商: Wiley
- 出版日期: 2007-06-01
- 售價: $1,800
- 貴賓價: 9.8 折 $1,764
- 語言: 英文
- 頁數: 454
- 裝訂: Hardcover
- ISBN: 0470027606
- ISBN-13: 9780470027608
-
相關分類:
Data-mining
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商品描述
Description
A comprehensive, coherent, and in depth presentation of the state of the art in fuzzy clustering.Fuzzy clustering is now a mature and vibrant area of research with highly innovative advanced applications. Encapsulating this through presenting a careful selection of research contributions, this book addresses timely and relevant concepts and methods, whilst identifying major challenges and recent developments in the area. Split into five clear sections, Fundamentals, Visualization, Algorithms and Computational Aspects, Real-Time and Dynamic Clustering, and Applications and Case Studies, the book covers a wealth of novel, original and fully updated material, and in particular offers:
- a focus on the algorithmic and computational augmentations of fuzzy clustering and its effectiveness in handling high dimensional problems, distributed problem solving and uncertainty management.
- presentations of the important and relevant phases of cluster design, including the role of information granules, fuzzy sets in the realization of human-centricity facet of data analysis, as well as system modelling
- demonstrations of how the results facilitate further detailed development of models, and enhance interpretation aspects
- a carefully organized illustrative series of applications and case studies in which fuzzy clustering plays a pivotal role
This book will be of key interest to engineers associated with fuzzy control, bioinformatics, data mining, image processing, and pattern recognition, while computer engineers, students and researchers, in most engineering disciplines, will find this an invaluable resource and research tool.
Table of Contents
List of Contributors.Foreword.
Preface.
Part I Fundamentals.
1 Fundamentals of Fuzzy Clustering (Rudolf Kruse, Christian Döring and Marie-Jeanne Lesot).
1.1 Introduction.
1.2 Basic Clustering Algorithms.
1.3 Distance Function Variants.
1.4 Objective Function Variants.
1.5 Update Equation Variants: Alternating Cluster Estimation.
1.6 Concluding Remarks.
Acknowledgements.
References.
2 Relational Fuzzy Clustering (Thomas A. Runkler).
2.1 Introduction.
2.2 Object and Relational Data.
2.3 Object Data Clustering Models.
2.4 Relational Clustering.
2.5 Relational Clustering with Non-spherical Prototypes.
2.6 Relational Data Interpreted as Object Data.
2.7 Summary.
2.8 Experiments.
2.9 Conclusions.
References.
3 Fuzzy Clustering with Minkowski Distance Functions (Patrick J.F. Groenen, Uzay Kaymak and Joost van Rosmalen).
3.1 Introduction.
3.2 Formalization.
3.3 The Majorizing Algorithm for Fuzzy C-means with Minkowski Distances.
3.4 The Effects of the Robustness Parameter.
3.5 Internet Attitudes.
3.6 Conclusions.
References.
4 Soft Cluster Ensembles (Kunal Punera and Joydeep Ghosh).
4.1 Introduction.
4.2 Cluster Ensembles.
4.3 Soft Cluster Ensembles.
4.4 Experimental Setup.
4.5 Soft vs. Hard Cluster Ensembles.
4.6 Conclusions and Future Work.
Acknowledgements.
References.
Part II Visualization.
5 Aggregation and Visualization of Fuzzy Clusters Based on Fuzzy Similarity Measures (János Abonyi and Balázs Feil).
5.1 Problem Definition.
5.2 Classical Methods for Cluster Validity and Merging.
5.3 Similarity of Fuzzy Clusters.
5.4 Visualization of Clustering Results.
5.5 Conclusions.
Appendix 5A.1 Validity Indices.
Appendix 5A.2 The Modified Sammon Mapping Algorithm.
Acknowledgements.
References.
6 Interactive Exploration of Fuzzy Clusters (Bernd Wiswedel, David E. Patterson and Michael R. Berthold).
6.1 Introduction.
6.2 Neighborgram Clustering.
6.3 Interactive Exploration.
6.4 Parallel Universes.
6.5 Discussion.
References.
Part III Algorithms and Computational Aspects.
7 Fuzzy Clustering with Participatory Learning and Applications (Leila Roling Scariot da Silva, Fernando Gomide and Ronald Yager).
7.1 Introduction.
7.2 Participatory Learning.
7.3 Participatory Learning in Fuzzy Clustering.
7.4 Experimental Results.
7.5 Applications.
7.6 Conclusions.
Acknowledgements.
References.
8 Fuzzy Clustering of Fuzzy Data (Pierpaolo D’Urso).
8.1 Introduction.
8.2 Informational Paradigm, Fuzziness and Complexity in Clustering Processes.
8.3 Fuzzy Data.
8.4 Fuzzy Clustering of Fuzzy Data.
8.5 An Extension: Fuzzy Clustering Models for Fuzzy Data Time Arrays.
8.6 Applicative Examples.
8.7 Concluding Remarks and Future Perspectives.
References.
9 Inclusion-based Fuzzy Clustering (Samia Nefti-Meziani and Mourad Oussalah).
9.1 Introduction.
9.2 Background: Fuzzy Clustering.
9.3 Construction of an Inclusion Index.
9.4 Inclusion-based Fuzzy Clustering.
9.5 Numerical Examples and Illustrations.
9.6 Conclusions.
Acknowledgements.
Appendix 9A.1.
References.
10 Mining Diagnostic Rules Using Fuzzy Clustering (Giovanna Castellano, Anna M. Fanelli and Corrado Mencar).
10.1 Introduction.
10.2 Fuzzy Medical Diagnosis.
10.3 Interpretability in Fuzzy Medical Diagnosis.
10.4 A Framework for Mining Interpretable Diagnostic Rules.
10.5 An Illustrative Example.
10.6 Concluding Remarks.
References.
11 Fuzzy Regression Clustering (Mikal Sato-Ilic).
11.1 Introduction.
11.2 Statistical Weighted Regression Models.
11.3 Fuzzy Regression Clustering Models.
11.4 Analyses of Residuals on Fuzzy Regression Clustering Models.
11.5 Numerical Examples.
11.6 Conclusion.
References.
12 Implementing Hierarchical Fuzzy Clustering in Fuzzy Modeling Using the Weighted Fuzzy C-means (George E. Tsekouras).
12.1 Introduction.
12.2 Takagi and Sugeno’s Fuzzy Model.
12.3 Hierarchical Clustering-based Fuzzy Modeling.
12.4 Simulation Studies.
12.5 Conclusions.
References.
13 Fuzzy Clustering Based on Dissimilarity Relations Extracted from Data (Mario G.C.A. Cimino, Beatrice Lazzerini and Francesco Marcelloni).
13.1 Introduction.
13.2 Dissimilarity Modeling.
13.3 Relational Clustering.
13.4 Experimental Results.
13.5 Conclusions.
References.
14 Simultaneous Clustering and Feature Discrimination with Applications (Hichem Frigui).
14.1 Introduction.
14.2 Background.
14.3 Simultaneous Clustering and Attribute Discrimination (SCAD).
14.4 Clustering and Subset Feature Weighting.
14.5 Case of Unknown Number of Clusters.
14.6 Application 1: Color Image Segmentation.
14.7 Application 2: Text Document Categorization and Annotation.
14.8 Application 3: Building a Multi-modal Thesaurus from Annotated Images.
14.9 Conclusions.
Appendix 14A.1.
Acknowledgements.
References.
Part IV Real-time and Dynamic Clustering.
15 Fuzzy Clustering in Dynamic Data Mining – Techniques and Applications (Richard Weber).
15.1 Introduction.
15.2 Review of Literature Related to Dynamic Clustering.
15.3 Recent Approaches for Dynamic Fuzzy Clustering.
15.4 Applications.
15.5 Future Perspectives and Conclusions.
Acknowledgement.
References.
16 Fuzzy Clustering of Parallel Data Streams (Jürgen Beringer and Eyke Hüllermeier).
16.1 Introduction.
16.2 Background.
16.3 Preprocessing and Maintaining Data Streams.
16.4 Fuzzy Clustering of Data Streams.
16.5 Quality Measures.
16.6 Experimental Validation.
16.7 Conclusions.
References.
17 Algorithms for Real-time Clustering and Generation of Rules from Data (Dimitar Filev and Plamer Angelov).
17.1 Introduction.
17.2 Density-based Real-time Clustering.
17.3 FSPC: Real-time Learning of Simplified Mamdani Models.
17.4 Applications.
17.5 Conclusion.
References.
Part V Applications and Case Studies.
18 Robust Exploratory Analysis of Magnetic Resonance Images using FCM with Feature Partitions (Mark D. Alexiuk and Nick J. Pizzi).
18.1 Introduction.
18.2 FCM with Feature Partitions.
18.3 Magnetic Resonance Imaging.
18.4 FMRI Analysis with FCMP.
18.5 Data-sets.
18.6 Results and Discussion.
18.7 Conclusion.
Acknowledgements.
References.
19 Concept Induction via Fuzzy C-means Clustering in a High-dimensional Semantic Space (Dawei Song, Guihong Cao, Peter Bruza and Raymond Lau).
19.1 Introduction.
19.2 Constructing a High-dimensional Semantic Space via Hyperspace Analogue to Language.
19.3 Fuzzy C-means Clustering.
19.4 Word Clustering on a HAL Space – A Case Study.
19.5 Conclusions and Future Work.
Acknowledgement.
References.
20 Novel Developments in Fuzzy Clustering for the Classification of Cancerous Cells using FTIR Spectroscopy (Xiao-Ying Wang, Jonathan M. Garibaldi, Benjamin Bird and Mike W. George).
20.1 Introduction.
20.2 Clustering Techniques.
20.3 Cluster Validity.
20.4 Simulated Annealing Fuzzy Clustering Algorithm.
20.5 Automatic Cluster Merging Method.
20.6 Conclusion.
Acknowledgements.
References.
Index.
商品描述(中文翻譯)
描述
模糊聚類是一個成熟且充滿活力的研究領域，擁有高度創新的先進應用。本書通過精心挑選的研究貢獻，全面、連貫且深入地介紹了模糊聚類的最新技術，並針對該領域的主要挑戰和近期發展進行了探討。本書分為五個清晰的部分：基礎、可視化、演算法與計算方面、即時與動態聚類，以及應用與案例研究，涵蓋了大量新穎、原創且完全更新的材料，特別提供：
- 對模糊聚類的演算法和計算增強的重點，及其在處理高維問題、分散式問題解決和不確定性管理中的有效性。
- 聚類設計的重要和相關階段的介紹，包括信息顆粒的角色、模糊集在數據分析人本性方面的實現，以及系統建模。
- 如何利用結果促進模型的進一步詳細發展，並增強解釋方面的示範。
- 一系列精心組織的應用和案例研究，其中模糊聚類扮演了關鍵角色。
本書將對與模糊控制、生物信息學、數據挖掘、圖像處理和模式識別相關的工程師特別感興趣，而計算機工程師、學生和研究人員在大多數工程學科中，將會發現這是一本寶貴的資源和研究工具。
目錄
貢獻者名單。
前言。
序言。
第一部分 基礎。
1 模糊聚類的基礎（Rudolf Kruse, Christian Döring 和 Marie-Jeanne Lesot）。
1.1 介紹。
1.2 基本聚類演算法。
1.3 距離函數變體。
1.4 目標函數變體。
1.5 更新方程變體：交替聚類估計。
1.6 總結。
致謝。
參考文獻。
2 關聯模糊聚類（Thomas A. Runkler）。
2.1 介紹。
2.2 對象和關聯數據。
2.3 對象數據聚類模型。
2.4 關聯聚類。
2.5 具有非球形原型的關聯聚類。
2.6 將關聯數據解釋為對象數據。
2.7 總結。
2.8 實驗。
2.9 結論。
參考文獻。
3 使用Minkowski距離函數的模糊聚類（Patrick J.F. Groenen, Uzay Kaymak 和 Joost van Rosmalen）。
3.1 介紹。
3.2 正式化。
3.3 使用Minkowski距離的模糊C均值的主要化演算法。
3.4 穩健性參數的影響。
3.5 網際網路態度。
3.6 結論。
參考文獻。
4 軟聚類集成（Kunal Punera 和 Joydeep Ghosh）。
4.1 介紹。
4.2 聚類集成。
4.3 軟聚類集成。
4.4 實驗設置。
4.5 軟聚類集成與硬聚類集成的比較。
4.6 結論與未來工作。
致謝。
參考文獻。
第二部分 可視化。
5 基於模糊相似性度量的模糊聚類的聚合與可視化（János Abonyi 和 Balázs Feil）。
5.1 問題定義。
5.2 聚類有效性和合併的經典方法。
5.3 模糊聚類的相似性。
5.4 聚類結果的可視化。
5.5 結論。
附錄5A.1 有效性指數。
附錄5A.2 修改的Sammon映射演算法。
致謝。
參考文獻。
6 模糊聚類的互動探索（Bernd Wiswedel, David E. Patterson 和 Michael R. Berthold）。
6.1 介紹。
6.2 鄰居圖聚類。
6.3 互動探索。
6.4 平行宇宙。
6.5 討論。
參考文獻。
第三部分 演算法與計算方面。
7 具有參與式學習和應用的模糊聚類（Leila Roling Scariot da Silva, Fernando Gomide 和 Ronald Yager）。
7.1 介紹。
7.2 參與式學習。
7.3 模糊聚類中的參與式學習。
7.4 實驗結果。
7.5 應用。
7.6 結論。
致謝。
參考文獻。
8 模糊數據的模糊聚類（Pierpaolo D’Urso）。
8.1 介紹。
8.2 信息範式、模糊性和聚類過程中的複雜性。
8.3 模糊數據。
8.4 模糊數據的模糊聚類。
8.5 擴展：模糊數據時間數組的模糊聚類模型。
8.6 應用示例。
8.7 總結與未來展望。
參考文獻。
9 基於包含的模糊聚類（Samia Nefti-Meziani 和 Mourad Oussalah）。
9.1 介紹。
9.2 背景：模糊聚類。
9.3 包含指數的構建。
9.4 基於包含的模糊聚類。
9.5 數值示例和插圖。
9.6 結論。
致謝。
附錄9A.1。
參考文獻。
10 使用模糊聚類挖掘診斷規則（Giovanna Castellano, Anna M. Fanelli 和 Corrado Mencar）。
10.1 介紹。
10.2 模糊醫學診斷。
10.3 模糊醫學診斷中的可解釋性。
10.4 挖掘可解釋診斷規則的框架。
10.5 一個示例。
10.6 總結。
參考文獻。
11 模糊回歸聚類（Mikal Sato-Ilic）。
11.1 介紹。
11.2 統計加權回歸模型。
11.3 模糊回歸聚類模型。
11.4 模糊回歸聚類模型的殘差分析。
11.5 數值示例。
11.6 結論。
參考文獻。
12 在模糊建模中使用加權模糊C均值實現層次模糊聚類（George E. Tsekouras）。
12.1 介紹。
12.2 Takagi和Sugeno的模糊模型。
12.3 基於層次聚類的模糊建模。
12.4 模擬研究。
12.5 結論。
參考文獻。
13 基於從數據中提取的相異性關係的模糊聚類（Mario G.C.A. Cimino, Beatrice Lazzerini 和 Francesco Marcelloni）。
13.1 介紹。
13.2 相異性建模。
13.3 關聯聚類。
13.4 實驗結果。
13.5 結論。
參考文獻。
14 同時聚類和特徵區分的應用（Hichem Frigui）。
14.1 介紹。
14.2 背景。
14.3 同時聚類和屬性區分（SCAD）。
14.4 聚類和子集特徵加權。
14.5 未知聚類數的情況。
14.6 應用1：彩色圖像分割。
14.7 應用2：文本文件分類和標註。
14.8 應用3：從標註圖像構建多模態詞庫。
14.9 結論。
附錄14A.1。
致謝。
參考文獻。
第四部分 即時與動態聚類。
15 在動態數據挖掘中的模糊聚類 - 技術與應用（Richard Weber）。
