PPT-Unsupervised Clickstream Clustering for User Behavior Analy

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. Mirbelunicefr Amadeus sas 485 Route du Pin Mon tard BP 69 06902 Sophia An tip olis Cedex rance vrebuffelamadeusnet Abstract User In terfaces UI are an essen tial part of most soft ares esp ecially data in tensiv ones as database or eb based applicati 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. 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?. 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. 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.. 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. Tao Xie. Joint Work w/ . David Yang, . Sihan Li (Illinois). Xusheng . Xiao, Benjamin Andow, . Rahul . Pandita, . William Enck (NCSU). Mobile App Markets. Apple App Store. Google Play. Microsoft Windows Phone. Xufei. Wang. , Lei Tang, . Huiji. . Gao. , and . Huan. Liu. xufei.wang@asu.edu. Arizona State University. Contact Information. Xufei. Wang. , . Huiji. . Gao. , and . Huan. Liu, Arizona State University. Based on Neutrosophic Set Theory. A. E. Amin. Department of Computer Science, Mansoura University, Mansoura 35516, Egypt. In this presentation, a new technique is used to an unsupervised learning image classification based on integration between . Eric Conte, Benjamin . Fuks. BATS meeting. Release 1.1.6. slide . 2. . Out the day before ACAT talk. : 17 May 2013. New C++ structure required for next major developments. A lot of new bugs …. mainly fixed. Thanks to Adam, Michael and Jose.. Sybil. D. etection. Gang Wang. , . Tristan Konolige, . Christo Wilson. †. , Xiao . Wang. ‡. . Haitao . Zheng and Ben Y. Zhao. UC Santa Barbara . †. Northeastern University . ‡. Renren . Inc.. Sybil. Detection. Gang Wang (. 王刚. ) . UC . Santa . Barbara. gangw@cs.ucsb.edu. Modeling User Clickstream Events. User-generated events. E.g. profile load, link follow, photo browse, friend invite. 2. Clustering. Agenda. Clustering Problem and Clustering Applications. Clustering Methodologies and Techniques. Graph-based clustering methods. K-Means and allocation-based methods. Hierarchical Agglomerative Clustering.