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商品描述
Discover Novel and Insightful Knowledge from Data Represented as a Graph
Practical Graph Mining with R presents a "do-it-yourself" approach to extracting interesting patterns from graph data. It covers many basic and advanced techniques for the identification of anomalous or frequently recurring patterns in a graph, the discovery of groups or clusters of nodes that share common patterns of attributes and relationships, the extraction of patterns that distinguish one category of graphs from another, and the use of those patterns to predict the category of new graphs.
Hands-On Application of Graph Data Mining
Each chapter in the book focuses on a graph mining task, such as link analysis, cluster analysis, and classification. Through applications using real data sets, the book demonstrates how computational techniques can help solve real-world problems. The applications covered include network intrusion detection, tumor cell diagnostics, face recognition, predictive toxicology, mining metabolic and protein-protein interaction networks, and community detection in social networks.
Develops Intuition through Easy-to-Follow Examples and Rigorous Mathematical Foundations
Every algorithm and example is accompanied with R code. This allows readers to see how the algorithmic techniques correspond to the process of graph data analysis and to use the graph mining techniques in practice. The text also gives a rigorous, formal explanation of the underlying mathematics of each technique.
Makes Graph Mining Accessible to Various Levels of Expertise
Assuming no prior knowledge of mathematics or data mining, this self-contained book is accessible to students, researchers, and practitioners of graph data mining. It is suitable as a primary textbook for graph mining or as a supplement to a standard data mining course. It can also be used as a reference for researchers in computer, information, and computational science as well as a handy guide for data analytics practitioners.
商品描述(中文翻譯)
從以圖形表示的數據中發現新穎且具洞察力的知識
使用 R 進行實用的圖形挖掘提供了一種「自己動手」的方法來從圖形數據中提取有趣的模式。它涵蓋了許多基本和進階技術,用於識別圖形中的異常或經常重複的模式、發現共享共同屬性和關係的節點群組或集群、提取區分不同類別圖形的模式,以及利用這些模式來預測新圖形的類別。
圖形數據挖掘的實踐應用
本書的每一章都專注於一個圖形挖掘任務,例如鏈接分析、聚類分析和分類。通過使用真實數據集的應用,本書展示了計算技術如何幫助解決現實世界中的問題。涵蓋的應用包括網絡入侵檢測、腫瘤細胞診斷、人臉識別、預測毒理學、挖掘代謝和蛋白質-蛋白質相互作用網絡,以及社交網絡中的社區檢測。
通過易於理解的範例和嚴謹的數學基礎來培養直覺
每個算法和範例都附有 R 代碼。這使讀者能夠看到算法技術如何與圖形數據分析的過程相對應,並在實踐中使用圖形挖掘技術。文本還對每種技術的基礎數學進行了嚴謹的正式解釋。
使圖形挖掘對各種專業水平的人員都能夠接觸
本書假設讀者沒有數學或數據挖掘的先前知識,這本自成一體的書籍對於圖形數據挖掘的學生、研究人員和從業者都很容易理解。它適合作為圖形挖掘的主要教科書或作為標準數據挖掘課程的補充資料。它也可以作為計算機、信息和計算科學研究人員的參考資料,以及數據分析從業者的實用指南。