Graph-Theoretic Techniques for Web Content Mining (Machine Perception and Artificial Intelligence) (Series in Machine Perception and Artificial Intelligence)

this Book Describes Exciting New Opportunities For Utilizing Robust Graph Representations Of Data With Common Machine Learning Algorithms. Graphs Can Model Additional Information Which Is Often Not Present In Commonly Used Data Representations, Such As Vectors. Through The Use Of Graph Distance - A Relatively New Approach For Determining Graph Similarity - The Authors Show How Well-known Algorithms, Such As K-means Clustering And K-nearest Neighbors Classification, Can Be Easily Extended To Work With Graphs Instead Of Vectors. This Allows For The Utilization Of Additional Information Found In Graph Representations, While At The Same Time Employing Well-known, Proven Algorithms. to Demonstrate And Investigate These Novel Techniques, The Authors Have Selected The Domain Of Web Content Mining, Which Involves The Clustering And Classification Of Web Documents Based On Their Textual Substance. Several Methods Of Representing Web Document Content By Graphs Are Introduced; An Interesting Feature Of These Representations Is That They Allow For A Polynomial Time Distance Computation, Something Which Is Typically An Np-complete Problem When Using Graphs. Experimental Results Are Reported For Both Clustering And Classification In Three Web Document Collections Using A Variety Of Graph Representations, Distance Measures, And Algorithm Parameters. in Addition, This Book Describes Several Other Related Topics, Many Of Which Provide Excellent Starting Points For Researchers And Students Interested In Exploring This New Area Of Machine Learning Further. These Topics Include Creating Graph-based Multiple Classifier Ensembles Through Random Node Selection And Visualization Of Graph-based Data Using Multidimensional Scaling. 1. Introduction to web mining. 1.1. Overview of web mining methodologies. 1.2. Traditional information retrieval techniques. 1.3. Overview of remaining chapters -- 2. Graph similarity techniques. 2.1. Graph and subgraph isomorphism. 2.2. Graph edit distance. 2.3. Maximum common subgraph / minimum common supergraph approach. 2.4. State space search approach. 2.5. Probabilistic approach. 2.6. Distance preservation approach. 2.7. Relaxation approaches. 2.8. Mean and median of graphs. 2.9. Summary -- 3. Graph models for web documents. 3.1. Pre-processing. 3.2. Graph representations of web documents. 3.3. Complexity analysis. 3.4. Web document data sets -- 4. Graph-based clustering. 4.1. The graph-based k-means clustering algorithm. 4.2. Clustering performance measures. 4.3. Comparison with previously published results. 4.4. Comparison of different graph-theoretical distance. 4.5. Comparison of clustering algorithms. 4.6. Visualization of graph clustering. 4.7. The graph-based global k-means algorithm -- 5. Graph-based classification. 5.1. The k-nearest neighbors algorithm. 5.2. Graph-based multiple classifier ensembles -- 6. The graph hierarchy construction algorithm for web search clustering. 6.1. Cluster Hierarchy Construction Algorithm (CHCA). 6.2. Application of CHCA to search results processing. 6.3. Examples of results. 6.4. Graph Hierarchy Construction Algorithm (GHCA). 6.5. Comments -- 7. Conclusions and future work "This book describes exciting new opportunities for utilizing robust graph representations of data with common machine learning algorithms. Graphs can model additional information which is often not present in commonly used data representations, such as vectors. Through the use of graph distance - a relatively new approach for determining graph similarity - the authors show how well-known algorithms, such as k-means clustering and k-nearest neighbors classification, can be easily extended to work with graphs instead of vectors. This allows for the utilization of additional information found in graph representations, while at the same time employing well-known, proven algorithms."--BOOK JACKET With the recent explosive growth of the amount of content on the Internet, it has become increasingly difficult for users to find and utilize information and for content providers to classify and catalog documents.