Pattern Recognition Algorithms for Data Mining
暫譯: 數據挖掘的模式識別演算法

Pal, Sankar K., Mitra, Pabitra

Pattern Recognition Algorithms for Data Mining
  • 出版商: CRC
  • 出版日期: 2019-09-19
  • 售價: $2,970
  • 貴賓價: 9.5 折 $2,822
  • 語言: 英文
  • 頁數: 280
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 0367394243
  • ISBN-13: 9780367394240
  • 相關分類: Algorithms-data-structures、Data-mining

    • 海外代購書籍(需單獨結帳)

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商品描述

Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks.

Organized into eight chapters, the book begins with an introduction to PR, data mining, and knowledge discovery concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with support vector machine (SVM). They conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm.

商品描述(中文翻譯)

《資料探勘的模式識別演算法》在統一的框架下探討不同的模式識別（PR）任務，並提供理論與實驗結果。涵蓋的任務包括資料濃縮、特徵選擇、案例生成、聚類/分類，以及規則生成與評估。本書呈現各種理論、方法論和演算法，使用經典方法與混合範式。作者強調處理具有重疊、難以處理或非線性邊界類別的大型資料集，以及在柔性框架中展示粒狀計算的資料集。

本書分為八章，首先介紹模式識別、資料探勘和知識發現的概念。作者分析多尺度資料濃縮和降維的任務，然後探討使用支持向量機（SVM）進行學習的問題。最後，他們強調粒狀計算在柔性範式中對不同探勘任務的重要性。

目錄大綱

Introduction. Multiscale data condensation. Unsupervised feature selection. Active learning using support vector machine. Rough-fuzzy case generation. Rough-fuzzy clustering. Rough self-organizing map. Classification, rule generation and evaluation using modular rough-fuzzy MLP. Appendices.

目錄大綱(中文翻譯)

Introduction. Multiscale data condensation. Unsupervised feature selection. Active learning using support vector machine. Rough-fuzzy case generation. Rough-fuzzy clustering. Rough self-organizing map. Classification, rule generation and evaluation using modular rough-fuzzy MLP. Appendices.

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