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. 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?. algorithm and its application in single . cell . RNA-. seq. data analysis and identification of gain or loss of functions of somatic mutations . Chi Zhang, Ph.D.. Center for Computational Biology and Bioinformatics. 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.. issue in . computing a representative simplicial complex. . Mapper does . not place any conditions on the clustering . algorithm. Thus . any domain-specific clustering algorithm can . be used.. We . Lecture outline. Distance/Similarity between data objects. Data objects as geometric data points. Clustering problems and algorithms . K-means. K-median. K-center. What is clustering?. A . grouping. of data objects such that the objects . 1. Mark Stamp. K-Means for Malware Classification. Clustering Applications. 2. Chinmayee. . Annachhatre. Mark Stamp. Quest for the Holy . Grail. Holy Grail of malware research is to detect previously unseen malware. 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. Produces a set of . nested clusters . organized as a hierarchical tree. Can be visualized as a . dendrogram. A tree-like diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering. AMB Review 11/2010. Consensus Clustering . (. Monti. et al. 2002). Internal validation method for clustering algorithms.. Stability based technique.. Can be used to compare algorithms or for estimating the number of clusters in the data.. 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.