Web Mining and Social Networking: Techniques and ... - tud.ttu.ee

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ContentsPart I Foundation1 Introduction ................................................... 31.1 Background . . ............................................. 31.2 Data Mining and Web Mining . . .............................. 51.3 Web Community and Social Network Analysis . . . ............... 71.3.1 Characteristics of Web Data . . ......................... 71.3.2 Web Community . . ................................... 81.3.3 Social Networking ................................... 91.4 Summary of Chapters ....................................... 101.5 Audience of This Book . . . ................................... 112 Theoretical Backgrounds ....................................... 132.1 Web Data Model . . . ........................................ 132.2 Textual, Linkage and Usage Expressions . . . .................... 142.3 Similarity Functions ........................................ 162.3.1 Correlation-based Similarity . . ......................... 172.3.2 Cosine-Based Similarity .............................. 172.4 Eigenvector, Principal Eigenvector . . . ......................... 172.5 Singular Value Decomposition (SVD) of Matrix . . ............... 192.6 Tensor Expression and Decomposition . ........................ 202.7 Information Retrieval Performance Evaluation Metrics . .......... 222.7.1 Performance measures . . .............................. 222.7.2 Web Recommendation Evaluation Metrics ............... 242.8 Basic Concepts in Social Networks . . . ......................... 252.8.1 Basic Metrics of Social Network . . . .................... 252.8.2 Social Network over the Web . ......................... 263 Algorithms and Techniques ..................................... 293.1 Association Rule Mining . ................................... 293.1.1 Association Rule Mining Problem . . .................... 29
Page 2 and 3: Web Mining and Social Networking
Page 4: Guandong Xu • Yanchun Zhang • L
Page 8 and 9: VIIIPrefacefollowing characteristic
Page 11: Acknowledgements: We would like to
Page 15 and 16: ContentsXV4.6.2 Opinion Spam . . .
Page 17: ContentsXVII8.5 Combinational CF Ap
Page 21 and 22: 1Introduction1.1 BackgroundWith the
Page 23 and 24: 1.2 Data Mining and Web Mining 5gin
Page 25 and 26: 1.3 Web Community and Social Networ
Page 27 and 28: 1.3 Web Community and Social Networ
Page 29: 1.5 Audience of This Book 11and use
Page 32 and 33: 14 2 Theoretical Backgroundsinforma
Page 34 and 35: 16 2 Theoretical Backgroundsalso be
Page 36 and 37: 18 2 Theoretical Backgroundsin line
Page 38 and 39: 20 2 Theoretical BackgroundsAs know
Page 40 and 41: 22 2 Theoretical BackgroundsThe bes
Page 42 and 43: 24 2 Theoretical Backgroundswould b
Page 44 and 45: 26 2 Theoretical BackgroundsBetween
Page 47 and 48: 3Algorithms and TechniquesApart fro
Page 49 and 50: Table 3.2. Frequent itemsets3.1 Ass
Page 51 and 52: 3.1 Association Rule Mining 33{appl
Page 53 and 54: Table 3.4. An example database for
Page 55 and 56: 3.1 Association Rule Mining 37proje
Page 57 and 58: 3.1 Association Rule Mining 39Table
Page 59 and 60: 3.1 Association Rule Mining 41abcdS
Page 61 and 62: SID TID {a} {b} {c} {d}100 1 1 0 0
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3.1 Association Rule Mining 45Custo
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3.2 Supervised Learning 47decision
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3.2 Supervised Learning 493.2.3 Bay
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3.2 Supervised Learning 51NY = f (
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3.3 Unsupervised Learning 53The key
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3.3 Unsupervised Learning 553.3.3 D
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3.4 Semi-supervised Learning 57Whil
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3.5 Markov Models 593.4.4 Graph bas
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3.5 Markov Models 61yy yt-1 tt+1xt-
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3.8 Collaborative Filtering Recomme
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3.9 Social Network Analysis 65have
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3.9 Social Network Analysis 673.9.2
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Part IIWeb Mining: Techniques and A
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72 4 Web Content Miningcollection t
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74 4 Web Content Miningarchiving th
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76 4 Web Content MiningExperimental
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78 4 Web Content MiningInput queryO
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80 4 Web Content Miningrelatively c
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82 4 Web Content MiningP(ω,d)=P(d)
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84 4 Web Content MiningWe will refe
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86 4 Web Content Miningopinions is
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5Web Linkage MiningIn the last chap
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5.3 PageRank and HITS Algorithms 91
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5.3 PageRank and HITS Algorithms 93
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5.4 Web Community Discovery 95• L
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5.4.2 Network Flow/Cut-based Notion
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5.4 Web Community Discovery 99(2) c
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5.5 Web Graph Measurement and Model
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5.6 Using Link Information for Web
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110 6 Web Usage Miningbe determined
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112 6 Web Usage Miningthe links (un
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114 6 Web Usage MiningDecomposition
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116 6 Web Usage Miningthe session-p
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118 6 Web Usage Mining6.2 Web Usage
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120 6 Web Usage MiningP(s i |z k )=
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122 6 Web Usage MiningExamples of L
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124 6 Web Usage Mining6.3 Finding U
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126 6 Web Usage MiningThere are two
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128 6 Web Usage MiningAlgorithm 6.7
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130 6 Web Usage Miningap k =∑s i
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132 6 Web Usage Mining6.4.2 An Exam
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134 6 Web Usage MiningWith the prop
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136 6 Web Usage MiningFig. 6.7. An
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138 6 Web Usage Mininghigh co-occur
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140 6 Web Usage MiningDocument clic
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142 6 Web Usage Mining(2) Degree of
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7Extracting and Analyzing Web Socia
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7.1 Extracting Evolution of Web Com
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7.2 Temporal Analysis on Semantic G
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7.2 Temporal Analysis on Semantic G
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7.3 Analysis of Communities and The
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7.3 Analysis of Communities and The
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7.4 Socio-Sense: A System for Analy
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8Web Mining and Recommendation Syst
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8.1 User-based and Item-based Colla
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8.1 User-based and Item-based Colla
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8.2 A Hybrid User-based and Item-ba
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Model Building8.2 A Hybrid User-bas
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8.3 User Profiling for Web Recommen
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8.3 User Profiling for Web Recommen
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8.4 Combing Long-Term Web Achieves
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8.5 Combinational CF Approach for P
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8.5 Combinational CF Approach for P
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9Conclusions9.1 SummaryNowadays Wor
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9.2 Future Directions 191to show th
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9.2 Future Directions 193The follow
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References1. http://dms.irb.hr/.2.
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References 19732. D. Billsus and M.
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References 19965. R. Cooley, B. Mob
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References 201hypermedia : links, o
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References 203136. M. Kitsuregawa,
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References 205167. B. Liu and K. Ch
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References 207204. M. Perkowitz and
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References 209241. J. Teevan, S. T.
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