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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. itemsets. : alternative representations and combinatorial problems. Too many frequent . itemsets. If {. a. 1. , . …. , a. 100. } . is a frequent . itemset. , then there are. . 1.27*10. 30 . frequent sub-patterns. Mining Frequent Patterns. Ali Javed. CS:332, April 20. th. , 2015. Slides by . Afsoon. . Yousefi. Jiawei. Han, . Jian. Pei and . Yiwen. Yin. . School of Computer Science. Simon Fraser University. Presented by . Yaron. . Gonen. Outline. Introduction. Problems definition and motivation. Previous work. The CAMLS Algorithm. Overview. Main contributions. Results. Future Work. Frequent Item-sets:. in Data Streams . at Multiple Time Granularities. CS525 Paper Presentation. Presented by:. Pei Zhang, . Jiahua. Liu, . Pengfei. . Geng. and . Salah. Ahmed. Authors: Chris . Giannella. , . Jiawei. Frequent . subgraph. mining algorithms that allows for label and structural mismatches in the . isomorphisms. are useful in many real world scenarios.. Problem Statement. Given a graph database, label match cost matrix, label mismatch threshold . Mining Sequential & Navigational Patterns. Bamshad Mobasher. DePaul . University. Sequential pattern mining. Association rule mining does not consider the order of transactions. . In many applications such orderings are significant. E.g., . Concordia Institute for Information Systems Engineering. Concordia University. Montreal, Canada. A Novel Approach of Mining Write-Prints for Authorship . Attribution in E-mail Forensics. Farkhund Iqbal. Mining Frequent Patterns. Afsoon. . Yousefi. CS:332, March 24. th. , 2014. Inspired by Song Wang slides. Jiawei. Han, . Jian. Pei and . Yiwen. Yin. . School of Computer Science. Simon Fraser University. itemsets. : alternative representations and combinatorial problems. Too many frequent . itemsets. If {. a. 1. , . …. , a. 100. } . is a frequent . itemset. , then there are. . 1.27*10. 30 . frequent sub-patterns. Chapter 7 : Advanced Frequent Pattern Mining. Jiawei Han, Computer Science, Univ. Illinois at Urbana-Champaign. , 2017. 1. October 28, 2017. Data Mining: Concepts and Techniques. 2. Chapter 7 : Advanced Frequent Pattern Mining. Chapter 6. . Mining Frequent Patterns, Association and Correlations: Basic Concepts and Methods. Jiawei Han, Computer Science, Univ. Illinois at Urbana-Champaign. , . 2017. 1. Chapter 6: Mining Frequent Patterns, Association and Correlations: Basic Concepts and Methods. mining algorithms that allows for label and structural mismatches in the . isomorphisms. are useful in many real world scenarios.. Problem Statement. Given a graph database, label match cost matrix, label mismatch threshold . By. Shailaja K.P. Introduction. Imagine that you are a sales manager at . AllElectronics. , and you are talking to a customer who recently bought a PC and a digital camera from the store. . What should you recommend to her next? . Dr. Sampath Jayarathna. Old Dominion University. . CS 495/595. Introduction to Data Mining. 1. Credit for some of the slides in this lecture goes to . Xun. Luo and Shun Liang. Introduction. Apriori.