PPT-DATA MINING LECTURE 5 Similarity and Distance

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. Presented by:. Akshay. Kumar. Pankaj. . Prateek. Are these similar?. Number ‘1’ vs. color ‘red’. Number ‘1’ vs. ‘small’. Horse vs. Rider. True vs. false . ‘. Monalisa. ’ vs. ‘Virgin of the rocks’. 12. Instructors:. http://www.cohenwang.com/edith/bigdataclass2013. Edith Cohen. Amos Fiat. Haim. Kaplan. Tova. Milo. Today. All-Distances Sketches. Applications of All-Distance sketches. Back to linear sketches (random linear transformations). 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). Input for Multidimensional Scaling and Clustering. Distances and Similarities. Both are ways of measuring how similar two objects are. Distances increase as objects are less similar. The distance of an object to itself is 0. Probability. Introduction to Biostatistics and Bioinformatics. Sequence Alignment Concepts . This Lecture. Sequence Alignment. Stuart M. Brown, Ph.D.. Center for Health Informatics and Bioinformatics. CSE, HKUST. March 20. Recap. String declaration. str1=“Hong”. str2=“Kong”. String Operators. strr. =str1+str2. “H” in . strr. String Slicing. strr. [. i. ]. strr. [:. i. ]. strr. [. i. :]. 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”. 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”. 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). Introduction to Data Mining. , 2. nd. Edition. by. Tan, Steinbach, Kumar. Outline. Attributes and Objects. Types of Data. Data Quality. Similarity and Distance. Data Preprocessing. What is Data?. Collection of . and Algorithms. Lecture Notes . for Chapter 7. Introduction to Data Mining. by. Tan, Steinbach, Kumar. Introduction to Data Mining, 2nd Edition Tan, Steinbach, . Karpatne. , Kumar. What is Cluster Analysis?. 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. 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.