PPT-Clustering CSC 575 Intelligent Information Retrieval

Author : caroline | Published Date : 2023-10-27

2 Clustering Agenda Clustering Problem and Clustering Applications Clustering Methodologies and Techniques Graphbased clustering methods KMeans and allocationbased

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Clustering CSC 575 Intelligent Information Retrieval: Transcript

2 Clustering Agenda Clustering Problem and Clustering Applications Clustering Methodologies and Techniques Graphbased clustering methods KMeans and allocationbased methods Hierarchical Agglomerative Clustering. - 0 - Patrice Bellot, Marc El-Bze Laboratoire d CSC 575. Intelligent Information Retrieval. Intelligent Information Retrieval. 2. Retrieval Models. Model is an idealization or abstraction of an actual process. in this case, process is matching of documents with queries, i.e., retrieval. 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 . 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. CSC 575. Intelligent Information Retrieval. Intelligent Information Retrieval. 2. Indexing. Indexing is the process of transforming items (documents) into a searchable data structure. creation of document surrogates to represent each document. and Indexing Models. CSC 575. Intelligent Information Retrieval. Information. need. Index. Pre-process. Parse. Collections. Rank. Query. text input. Lexical analysis and stop words. Result. Sets. How is. 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 . CSC 575. Intelligent Information Retrieval. Intelligent Information Retrieval. 2. Web Mining. Today. Overview of Web Data Mining. Web Content Mining / Text Mining. Web Usage Mining. Web Personalization. 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. .

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