PPT-DATA MINING LECTURE 5 Similarity and Distance

Author : joy | Published Date : 2024-07-10

Sketching Locality Sensitive Hashing SIMILARITY AND DISTANCE Thanks to Tan Steinbach and Kumar Introduction to Data Mining Rajaraman and Ullman Mining Massive

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DATA MINING LECTURE 5 Similarity and Distance: Transcript

Sketching Locality Sensitive Hashing SIMILARITY AND DISTANCE Thanks to Tan Steinbach and Kumar Introduction to Data Mining Rajaraman and Ullman Mining Massive Datasets Similarity and Distance. Bamshad Mobasher. DePaul University. Distance or Similarity Measures. Many data mining and analytics tasks involve the comparison of objects and determining . their . similarities (or dissimilarities). energies. D.A. . Artemenkov. , G.I. . . Lykasov. , . A.I. . . Malakhov. Joint Institute for Nuclear Research. malakhov@lhe.jinr.ru. Hadron Structure 2015, June 29 – July 3, 2015, . Horn. ý. . . . Multi-label Protein Subcellular Localization. Shibiao WAN and Man-Wai MAK. The Hong Kong Polytechnic University. Sun-Yuan KUNG. Princeton University. Outline. Introduction and Motivation. Retrieval of GO Terms. from . GOMMA. Michael . Hartung. , Lars Kolb, . Anika. . Groß. , Erhard Rahm. Database . Research Group. University of . Leipzig. 9th . Intl. . . Conf. . on Data Integration. in . the. Life . Sciences. Warm Up. 1.. . If . ∆. QRS.  . ∆. ZYX. , identify the pairs of congruent angles and the pairs of congruent sides.. Solve each proportion.. 2.. . 3.. . . x . = 9. x . = 18. . Q. Jing . Zhang, . Jie. . Tang . , Cong . Ma . , . Hanghang. . Tong . , Yu . Jing . , and . Juanzi. . Li. Presented by Moumita Chanda Das . Outline. Introduction. Problem formulation. Panther using path sampling. Centrality. , Similarity, and . Influence. Edith Cohen . Tel Aviv University. Graph Datasets:. Represent relations between “things”. Bowtie structure of the Web . Broder. et. al. 2001. Dolphin interactions. CS246: Mining Massive Datasets. Jure Leskovec, . Stanford University. http://cs246.stanford.edu. Recap: Finding similar documents. Task:. . Given a large number (. N. in the millions or billions) of documents, find “near duplicates”. Warm Up. Solve each proportion.. 1.. . . 2.. . 3.. 4. . If . ∆. QRS . ~ . ∆. XYZ. , identify the pairs of congruent angles and write 3 proportions using pairs of corresponding sides.. . Bamshad Mobasher. DePaul University. Distance or Similarity Measures. Many data mining and analytics tasks involve the comparison of objects and determining in terms of their similarities (or dissimilarities). http://www.cs.uic.edu/~. liub. CS583, Bing Liu, UIC. 2. General Information. Instructor: Bing Liu . Email: liub@cs.uic.edu . Tel: (312) 355 1318 . Office: SEO 931 . Lecture . times: . 9:30am-10:45am. Function approximation does not work: . F(x): x->y, x feature vector, y: label, but we don’t know y yet. Patterns may still exist (depending on the relationship between records). What is clustering. Jessica Chackoria. 1. , Brooke Nyberg. 1. , Melissa Vazquez. 1 . Suzanne Bell. 2. , . Alla. Vinokhodova. 3. , Vadim Gushin. 3. , Leslie DeChurch. 4 . , Noshir Contractor. 4 . 1. DePaul University, . Financial Services. Dhagash. Mehta. BlackRock, Inc.. Disclaimer: The views expresses here are those of the authors alone and not of BlackRock, Inc.. Introduction: Similarity. Scene from Alice’s Adventures in Wonderland by Lewis Carroll, 1865. Artist: John Tenniel.

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