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Data Mining Reading and Sample Application

This overview discusses various data mining methodologies and their applications across different domains. It highlights key conferences and workshops such as SIGMOD, KDD, and ICML, focusing on machine learning, sentiment analysis, and social network analysis. The paper presents structured prediction, deep learning approaches in web design, and explores crowdsourcing for data collection. Noteworthy contributions include language-independent sentiment mining models for platforms like Twitter, as well as advanced algorithms for characterizing user behavior in social networks.

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Data Mining Reading and Sample Application

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  1. Data Mining Reading and Sample Application Xietao Sept. 2013

  2. outline • Basic Info • Paper Glance

  3. Basic Info • SIGMOD VLDB ICDE PODS --- Database • KDD --- Data Mining • SNA-KDD --- workshop on SNS • ICML --- Machine Learning • SIGIR --- Info retrieval

  4. Course&Tools • Andrew Moore • Coursera (Bio-Datamining) • OCW-MIT • Weka --- waikato (New Zealand) • Rapid Miner --- Yale • IlliMine --- UIUC • Alpha Miner --- HKU • Potter’s wheel A-B-C --- UCB

  5. Paper Glance • ICML: • Neural Network\PCA\SVM\Framework • “Data-driven Web Design” • KDD: • Algorithm\Classfier\SNS\Cluster\Singular • “Learning from Crowds in the presence of Schools of Thought” • SNA-KDD • Twitter\Facebook\Weibo\Influence\Rumor • “Language-independent Bayesian sentiment mining on Twitter ”

  6. Data-Driven Web Design • Conf: ICML 2012 • Author: • Ranjitha Kumar Stanford University • Jerry O. TaltonIntel Corporation • Salman Ahmad MIT • Scott R. KlemmerStanford University

  7. Abstract • Applying machine learning methods to web design problems • Structured prediction • Deep learning • Probabilistic program induction • Enable useful interactions for designers

  8. Detail • Structured prediction : Rapid retargeting • Deep learning : Design-based Search • Probabilistic program induction : Operationalizing design patterns

  9. Learning from Crowds in the Presence ofSchools of Thought • Conf : KDD 2012 • Author: • YuandongTian CMU • Jun Zhu THU

  10. Abstract • Crowdsourcing: effective way to collect large-scale experimental data from distributed workers • Target: Identify reliable workers as well as unambiguous tasks

  11. Detail • Gold standard: task is objective with one correct answer • Schools of thought: each task may have multiple valid answers

  12. Language-independent Bayesian sentiment mining on TwitterLanguage-independent Bayesian sentiment mining on Twitter • Conf : SNA-KDD 2011 • Author: • Alex Davies University of Cambridge • ZoubinGhahramani University of Cambridge

  13. Abstract • New Language-independent model for sentiment analysis of short, social-network statues • Machine learning \ Bayesian Classfier

  14. Detail • Tweet is short, Senti-Icon shows a lot • Asymmetric Dirichletdistribution for word probability on sentiment • Iteratively Update the distribution and compute the probabilily

  15. More • “Joint Optimization of Bid and Budget Allocation in Sponsored Search” • KDD 2012 by SJTU • “Analysis and identification of spamming behaviors in SinaWeibomicroblog” • SNA-KDD 2013 by SJTU

  16. Thanks!!!

  17. Q&A

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