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  1. Ensemble methods in data mining [electronic resource] : improving accuracy through combining predictions

    Seni, Giovanni
    San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool Publishers, c2010.

    Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges - from investment timing to drug discovery, and fraud detection to recommendation systems - where predictive accuracy is more vital than model interpretability. Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly. After describing trees and their strengths and weaknesses, the authors provide an overview of regularization - today understood to be a key reason for the superior performance of modern ensembling algorithms. The book continues with a clear description of two recent developments: Importance Sampling (IS) and Rule Ensembles (RE). IS reveals classic ensemble methods - bagging, random forests, and boosting - to be special cases of a single algorithm, thereby showing how to improve their accuracy and speed. REs are linear rule models derived from decision tree ensembles. They are the most interpretable version of ensembles, which is essential to applications such as credit scoring and fault diagnosis. Lastly, the authors explain the paradox of how ensembles achieve greater accuracy on new data despite their (apparently much greater) complexity. This book is aimed at novice and advanced analytic researchers and practitioners - especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques.Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges - from investment timing to drug discovery, and fraud detection to recommendation systems - where predictive accuracy is more vital than model interpretability. Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly. After describing trees and their strengths and weaknesses, the authors provide an overview of regularization - today understood to be a key reason for the superior performance of modern ensembling algorithms. The book continues with a clear description of two recent developments: Importance Sampling (IS) and Rule Ensembles (RE). IS reveals classic ensemble methods - bagging, random forests, and boosting - to be special cases of a single algorithm, thereby showing how to improve their accuracy and speed. REs are linear rule models derived from decision tree ensembles. They are the most interpretable version of ensembles, which is essential to applications such as credit scoring and fault diagnosis. Lastly, the authors explain the paradox of how ensembles achieve greater accuracy on new data despite their (apparently much greater) complexity. This book is aimed at novice and advanced analytic researchers and practitioners -- especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques. The authors are industry experts in data mining and machine learning who are also adjunct professors and popular speakers. Although early pioneers in discovering and using ensembles, they here distill and clarify the recent groundbreaking work of leading academics (such as Jerome Friedman) to bring the benefits of ensembles to practitioners.

    Online Synthesis Digital Library

  2. Link mining : models, algorithms, and applications

    New York : Springer, c2010.

    With the recent ?ourishing research activities on Web search and mining, social networkanalysis, informationnetworkanalysis, informationretrieval, linkana- sis, andstructuraldatamining, researchonlinkmininghasbeenrapidlygrowing, forminganew?eldofdatamining. Traditionaldataminingfocuseson"?at"or"isolated"datainwhicheachda ta objectisrepresentedasanindependentattributevector. However, manyreal-world data sets are inter-connected, much richer in structure, involving objects of h- erogeneoustypesandcomplexlinks. Hence, thestudyoflinkminingwillhavea highimpactonvariousimportantapplicationssuchasWebandtextmining, social networkanalysis, collaborative?ltering, andbioinformatics. Asanemergingresearch?eld, therearecurrentlynobooksfocusingonthetheory andtechniquesaswellastherelatedapplicationsforlinkmining, especiallyfrom aninterdisciplinarypointofview. Ontheotherhand, duetothehighpopularity oflinkagedata, extensiveapplicationsrangingfromgovernmentalorganizationsto commercial businesses to people's daily life call for exploring the techniques of mininglinkagedata. Therefore, researchersandpractitionersneedacomprehensive booktosystematicallystudy, furtherdevelop, andapplythelinkminingtechniques totheseapplications. Thisbookcontainscontributedchaptersfromavarietyofprominentresear chers inthe?eld. Whilethechaptersarewrittenbydifferentresearchers, thetopicsand contentareorganizedinsuchawayastopresentthemostimportantmodels, al- rithms, andapplicationsonlinkmininginastructuredandconciseway. Giventhe lackofstructurallyorganizedinformationonthetopicoflinkmining, thebookwill provideinsightswhicharenoteasilyaccessibleotherwise. Wehopethatthebook willprovideausefulreferencetonotonlyresearchers, professors, andadvanced levelstudentsincomputersciencebutalsopractitionersinindustry. Wewouldliketoconveyourappreciationtoallauthorsfortheirvaluablec- tributions. WewouldalsoliketoacknowledgethatthisworkissupportedbyNSF throughgrantsIIS-0905215, IIS-0914934, andDBI-0960443. Chicago, Illinois PhilipS. Yu Urbana-Champaign, Illinois JiaweiHan Pittsburgh, Pennsylvania ChristosFaloutsos v Contents Part I Link-Based Clustering 1 Machine Learning Approaches to Link-Based Clustering...3 Zhongfei(Mark)Zhang, BoLong, ZhenGuo, TianbingXu, andPhilipS. Yu 2 Scalable Link-Based Similarity Computation and Clustering...45 XiaoxinYin, JiaweiHan, andPhilipS. Yu 3 Community Evolution and Change Point Detection in Time-Evolving Graphs...73 JimengSun, SpirosPapadimitriou, PhilipS. Yu, andChristosFaloutsos Part II Graph Mining and Community Analysis 4 A Survey of Link Mining Tasks for Analyzing Noisy and Incomplete Networks...107 GalileoMarkNamata, HossamSharara, andLiseGetoor 5 Markov Logic: A Language and Algorithms for Link Mining...135 PedroDomingos, DanielLowd, StanleyKok, AniruddhNath, Hoifung Poon, MatthewRichardson, andParagSingla 6 Understanding Group Structures and Properties in Social Media...163 LeiTangandHuanLiu 7 Time Sensitive Ranking with Application to Publication Search...187 XinLi, BingLiu, andPhilipS. Yu 8 Proximity Tracking on Dynamic Bipartite Graphs: Problem De?nitions and Fast Solutions...211 Hanghang Tong, Spiros Papadimitriou, Philip S. Yu, andChristosFaloutsos vii viii Contents 9 Discriminative Frequent Pattern-Based Graph Classi?cation...237 HongCheng, XifengYan, andJiaweiHan Part III Link Analysis for Data Cleaning and Information Integration 10 Information Integration for Graph Databases...2 65 Ee-PengLim, AixinSun, AnwitamanDatta, andKuiyuChang 11 Veracity Analysis and Object Distinction...283 XiaoxinYin, JiaweiHan, andPhilipS. Yu Part IV Social Network Analysis 12 Dynamic Community Identi?cation...

    Online SpringerLink

  3. Mathematical methods for knowledge discovery and data mining

    Hershey, PA : Information Science Reference, 2008.

    Focuses on the mathematical models and methods that support most data mining applications and solution techniques.

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