Practical Applications of Data Mining (Paperback)

Practical Applications of Data Mining emphasizes both theory and applications of data mining algorithms. Various topics of data mining techniques are identified and described throughout, including clustering, association rules, rough set theory, probability theory, neural networks, classification, and fuzzy logic. Each of these techniques is explored with a theoretical introduction and its effectiveness is demonstrated with various chapter examples. This book will help any database and IT professional understand how to apply data mining techniques to real-world problems. Following an introduction to data mining principles, Practical Applications of Data Mining introduces association rules to describe the generation of rules as the first step in data mining. It covers classification and clustering methods to show how data can be classified to retrieve information from data. Statistical functions and rough set theory are discussed to demonstrate how statistical and rough set formulas can be used for data analytics and knowledge discovery. Neural networks is an important branch in computational intelligence. It is introduced and explored in the text to investigate the role of neural network algorithms in data analytics.

《資料探勘的實際應用》強調資料探勘演算法的理論和應用。全書涵蓋了各種資料探勘技術，包括分群、關聯規則、粗糙集理論、概率理論、神經網絡、分類和模糊邏輯。每種技術都有理論介紹，並通過各章節的實例展示其有效性。本書將幫助任何資料庫和IT專業人士了解如何將資料探勘技術應用於實際問題。在介紹資料探勘原則後，《資料探勘的實際應用》介紹了關聯規則，將其描述為資料探勘的第一步。書中還涵蓋了分類和分群方法，展示了如何將資料分類以從中擷取資訊。還討論了統計函數和粗糙集理論，展示了如何使用統計和粗糙集公式進行資料分析和知識發現。神經網絡是計算智能的重要分支，本書介紹並探討了神經網絡演算法在資料分析中的作用。