PPT-Unsupervised Clickstream Clustering for User Behavior Analysis

Gang Wang Xinyi Zhang Shiliang Tang Haitao Zheng and Ben Y Zhao UC Santa Barbara gangwcsucsbedu Online Services Are UserDriven Huge user populations in todays online services. Natural language processing. Manaal Faruqui. Language Technologies Institute. SCS, CMU. Natural Language Processing. +. Linguistics. Computer Science. Natural Language Processing. But Why ?. I. nability to handle large amount of data. 1. Unsupervised Learning and Clustering. In unsupervised learning you are given a data set with no output classifications. Clustering is an important type of unsupervised learning. PCA was another type of unsupervised learning. Jaime . Teevan. , Microsoft . Reseach. UNC 2015. David Foster Wallace. Mark Twain. Cowards die many times . before their deaths. . Annotated by Nelson Mandela. I . have discovered a truly marvelous proof . Bing Zhang. Department of Biomedical Informatics. Vanderbilt University. bing.zhang@vanderbilt.edu. Overall workflow of gene expression studies. Microarray. Biological question. Experimental . design. Discovering Objects with Predictable Context. Carl . Doersch. , . Abhinav. Gupta, Alexei . Efros. Unsupervised Object Discovery. Children learn to see without millions of labels. Is there a cue hidden in the data that we can use to learn better representations?. Gang Wang. , Xinyi Zhang, . Shiliang. Tang,. Haitao. . Zheng. and Ben Y. Zhao. UC Santa Barbara . gangw@cs.ucsb.edu. Online Services Are User-Driven. Huge user populations in today’s online services. Rare . Event. Analysis with Multiple Failure Region Coverage. Wei Wu. 1. , Srinivas Bodapati. 2. , Lei He. 1,3. 1 Electrical Engineering Department, UCLA. 2 Intel Corporation. 3 . State . Key Laboratory of ASIC and Systems, . A CHI 2011 course. v11. Susan . Dumais. , Robin Jeffries, Daniel M. Russell, Diane Tang, Jaime . Teevan. CHI Tutorial, May, 2011. 1. Introduction. Daniel M. Russell . Google. 2. What Can We (HCI) Learn from Log Analysis? . Giuseppe M. Mazzeo. joint work with Elio Masciari and Carlo Zaniolo. Why a new clustering algorithm?. U. 2. -Clubs offers major advantages over current clustering algorithms. Totally unsupervised. Significantly faster. . . Chong Ho Yu. Why do we look at . grouping (cluster) patterns?. This regression model yields 21% variance explained.. The . p. value is not significant (p=0.0598). But remember we must look at (visualize) the data pattern rather than reporting the numbers. via Subspace Clustering. Ruizhen. Hu . Lubin. Fan . Ligang. Liu. Co-segmentation. Hu et al.. Co-Segmentation of 3D Shapes via Subspace Clustering. 2. Input. Co-segmentation. Hu et al.. A short tutorial…. Susan . Dumais. , Robin Jeffries, Daniel M. Russell, Diane Tang. , Jaime Teevan. HCIC Feb, 2010. What can we (HCI) learn from logs analysis? . Logs are the traces of human behavior. Sybil. D. etection. Gang Wang. , . Tristan Konolige, . Christo Wilson. †. , Xiao . Wang. ‡. . Haitao . Zheng and Ben Y. Zhao. UC Santa Barbara . †. Northeastern University . ‡. Renren . Inc.. Log. 2. transformation. Row centering and normalization. Filtering. Log. 2. Transformation. Log. 2. -transformation makes sure that the noise is independent of the mean and similar differences have the same meaning along the dynamic range of the values..