Rough-Fuzzy Pattern Recognition: Applications in Bioinformatics and Medical Imaging (Hardcover)

Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processing

Emphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy computing techniques to build working pattern recognition models. The authors explain step by step how to integrate rough sets with fuzzy sets in order to best manage the uncertainties in mining large data sets. Chapters are logically organized according to the major phases of pattern recognition systems development, making it easier to master such tasks as classification, clustering, and feature selection.

Rough-Fuzzy Pattern Recognition examines the important underlying theory as well as algorithms and applications, helping readers see the connections between theory and practice. The first chapter provides an introduction to pattern recognition and data mining, including the key challenges of working with high-dimensional, real-life data sets. Next, the authors explore such topics and issues as:

• Soft computing in pattern recognition and data mining

• A Mathematical framework for generalized rough sets, incorporating the concept of fuzziness in defining the granules as well as the set

• Selection of non-redundant and relevant features of real-valued data sets

• Selection of the minimum set of basis strings with maximum information for amino acid sequence analysis

• Segmentation of brain MR images for visualization of human tissues

Numerous examples and case studies help readers better understand how pattern recognition models are developed and used in practice. This text—covering the latest findings as well as directions for future research—is recommended for both students and practitioners working in systems design, pattern recognition, image analysis, data mining, bioinformatics, soft computing, and computational intelligence.

學習如何應用粗糙模糊計算技術解決生物信息學和醫學影像處理問題

強調在生物信息學和醫學影像處理中的應用，本書提供了一個清晰的框架，使讀者能夠利用最新的粗糙模糊計算技術建立工作中的模式識別模型。作者逐步解釋了如何將粗糙集與模糊集集成在一起，以便更好地處理挖掘大數據集中的不確定性。根據模式識別系統開發的主要階段，章節按邏輯順序組織，使學習分類、聚類和特徵選擇等任務更加容易。

《粗糙模糊模式識別》探討了重要的基礎理論、算法和應用，幫助讀者理解理論與實踐之間的聯繫。第一章介紹了模式識別和數據挖掘，包括處理高維度實際數據集的關鍵挑戰。接下來，作者探討了以下主題和問題：

- 模式識別和數據挖掘中的軟計算
- 通過將模糊性概念納入到定義粒子和集合中的廣義粗糙集的數學框架
- 選擇非冗餘和相關特徵的實值數據集
- 選擇具有最大信息的最小基礎字符串集進行氨基酸序列分析
- 大腦MR影像的分割，以可視化人體組織

大量的例子和案例研究幫助讀者更好地理解模式識別模型的開發和實際應用。本書涵蓋了最新的研究發現和未來研究方向，建議給系統設計、模式識別、影像分析、數據挖掘、生物信息學、軟計算和計算智能等領域的學生和從業人員閱讀。