PPT-Mining Interesting Locations and Travel Sequences from GP

Yu Zheng Lizhu Zhang Xing Xie WeiYing Ma Microsoft Research Asia Attack Overall score 1 Definite reject Reviewer confidence 4 High confidence Technical merit 2 Fair . Give an example of a problem that might benefit from feature creation . Compute the Silhouette of the following clustering that consists of 2 clusters: {(0,0), (0,1), (2,2)}. {(3,2), (3,3)}. . 1. Compare . AGNES /Hierarchical clustering with K-means; what are the main . differences?. 2 Compute the Silhouette of the following clustering that consists of 2 clusters: {(0,0), (0,1), (2,2)}. Mining the Most Interesting RulesRoberto J. Bayardo Jr.IBM Almaden Research Centerhttp://www.almaden.ibm.com/cs/people/bayardo/bayardo@alum.mit.eduRakesh AgrawalIBM Almaden Research Centerhttp://www.a Term. Nth term rule. Linear. Quadratic. Pattern. Sequence. Interesting fact. Have you heard about the Fibonacci sequence? Google it and write an interesting fact here!. My space. Write down 3 sequences, one of which, must be a quadratic, for the class to solve. Write down the nth term in brackets. Jeong. , . Dongseok. There are two techniques used for Video Fingerprinting : CPF(Color Patches Features) and Gradient Histograms. What is the main idea of these techniques?. What methods are used for similar image searching?. Mining the Most Interesting RulesRoberto J. Bayardo Jr.IBM Almaden Research Centerhttp://www.almaden.ibm.com/cs/people/bayardo/bayardo@alum.mit.eduRakesh AgrawalIBM Almaden Research Centerhttp://www.a CALIFORNIA . COMMUNITY COLLEGE. STUDENT COURSE SEQUENCES. Bruce Ingraham, . EdD. CAIR 2016, Los Angeles. Frequent Patterns in CCC Student Course Sequences. Outline. Introduction. Student Typologies. Lingering at community college. . Can I develop my use of varied and interesting vocabulary to create a country?. Starter:. If you could invent your own country what. would you name it?. Writers have been creating countries and worlds for years…. PSY505. Spring term, 2012. March 26, 2012. Today’s Class. Sequential Pattern Mining. Related to. Association Rule Mining. MOTIF Extraction. Similarities. MOTIF Extraction can be seen as a type of sequential pattern mining. Number Sequence. HW#1. Look-and-say sequence. 1, 11, 21, 1211, 111221, 312211, 13112221, 1113213211, 31131211131221 ……. 後一個數為前一個數的讀法，例如. 1. 讀作. one one. ，故下一個數為. COSC 6335 Student Presentations on Nov. 17, 2011. [Group1. ] . Amalaman,Paul. . Koutoua. ; . Joshi,Sushil. ; . Kampalli. . Santhamurthy,Divya. . Durga. : . A Study on Data Pre-processing for Mining the Global Terrorism Database. and Jiawei Han University of Illinois at Urbana-ChampaignPresented by: Yi-Hung Wu Closed Frequent Sequence Mining Where will data mining research go? Data Knowledge Action Frequent Itemsets, Associati or . Inspiring. . Sequences. Number. Hw1 An interesting sequence r06922114. 陳啟元. The sequence : . 1, 2, 4, 8, 16, 22, . 26, 38, 62, 74, 102. , . …. which is defined by . a. (n+1) . = . a(n) . Another Introduction to Data Mining. Course Information. 2. Knowledge Discovery in Data [and Data Mining] (KDD). Let us find something interesting!. Definition. := . “KDD is the non-trivial process of identifying valid, novel, potentially...