PDF-A Clustering Method for Information Retrieval

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0 Patrice Bellot Marc ElB143ze Laboratoire d

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A Clustering Method for Information Retrieval: Transcript

0 Patrice Bellot Marc ElB143ze Laboratoire d. Hui Fang , Tao . Tao. , . ChengXiang. . Zhai. University of Illinois at Urbana Champaign. SIGIR 2004 Best Paper. Presented by Lingjie Zhang. Outline. Formal Definitions of Heuristic Retrieval Constraints. CSC . 575. Intelligent Information Retrieval. 2. Source: . Intel. How much information?. Google: . ~100 . PB a . day; 3+ million servers (15 . Exabytes. stored). Wayback Machine has . ~9 . PB + . 100 . Date :. . 2012 . / . 04. . / . 12. 資訊碩一 . 10077034. 蔡勇儀 . @. . LAB603 . Outline. Introduction. Preliminaries. Method. Experimental result. Conclusions. Introduction. Image retrieval have more challenge than text retrieval.. CSC 575. Intelligent Information Retrieval. Intelligent Information Retrieval. 2. Clustering Techniques and IR. Today. Clustering Problem and Applications. Clustering Methodologies and Techniques. Applications of Clustering in IR. 0. .. The Basic Optimization Problem:. 2,4. I. 0. is an information rate. q ≔ q(T|Y) is a conditional probability that maps Y to T. Δ ≔ {q(T|Y) : }. I(X;T) ≔ . I(Y;T) ≔. Information Retrieval. Information Retrieval. Konsep. . dasar. . dari. IR . adalah. . pengukuran. . kesamaan. sebuah. . perbandingan. . antara. . dua. . dokumen. , . mengukur. . sebearapa. . ChengXiang. (“Cheng”) . . Zhai. Department of Computer Science. University of Illinois at Urbana-Champaign. http://www.cs.uiuc.edu/homes/czhai. . Email: czhai@illinois.edu. 1. Yahoo!-DAIS Seminar, UIUC. All slides ©Addison Wesley, 2008. Classification and Clustering. Classification and clustering are classical pattern recognition / machine learning problems. Classification. Asks “what class does this item belong to?”. Hongning. Wang. CS@UVa. What is information retrieval?. CS6501: Information Retrieval. CS@UVa. 2. Why information retrieval . Information overload. “. It refers to the . difficulty. a person can have understanding an issue and making decisions that can be caused by the presence of . What is clustering?. Why would we want to cluster?. How would you determine clusters?. How can you do this efficiently?. K-means Clustering. Strengths. Simple iterative method. User provides “K”. All slides ©Addison Wesley, 2008. How Much Data is Created Every . Minute?. Source: . https. ://www.domo.com/blog/2012/06/how-much-data-is-created-every-minute/. The Search Problem. Search and Information Retrieval. What is IR?. Sit down before fact as a little child, . be prepared to give up every conceived notion, . follow humbly wherever and whatever abysses nature leads, . or you will learn nothing. . . -- Thomas Huxley --. Fatemeh. Azimzadeh. Books. (Manning et al., 2008). Christopher D. Manning, . Prabhakar. . Raghavan. , and . Hinrich. . Schütze. . Introduction to Information Retrieval. Cambridge University Press, 2008. . 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.

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