PPT-Mining Top-K Association Rules

Comparison Group11 LEUNG Chung Yin Please go to the following link for the most updated version httpswwwdropboxcoms2u6g3z6ichv3t6cTopKRulespptxdl0 Once upon a time Philippe FournierViger1. Association rules . Given a set of . transactions . D. , . find rules that will predict the occurrence of an item (or a set of items) based on the occurrences of other items in the transaction. Market-Basket transactions. Chapter 1. Kirk Scott. Iris . virginica. 2. Iris . versicolor. 3. Iris . setosa. 4. 1.1 Data Mining and Machine Learning. 5. Definition of Data Mining. The process of discovering patterns in data.. (The patterns discovered must be meaningful in that they lead to some advantage, usually an economic one.). Emre Eftelioglu. 1. What is Knowledge Discovery in Databases?. Data mining is actually one step of a larger process known as . knowledge discovery in databases. (KDD).. The KDD process model consists of six phases. Data Mining and Knowledge Discovery . Prof. Carolina Ruiz and Weiyang Lin. Department of Computer Science. Worcester Polytechnic Institute. Sample Applications. Sample Commercial Applications. Market basket analysis. Debapriyo Majumdar. Data Mining – Fall 2014. Indian Statistical Institute Kolkata. August 4 and 7, 2014. Transaction id. Items. 1. Bread, Ham, Juice,. Cheese, Salami, Lettuce. 2. Rice, . Dal, Coconut, Curry leaves, Coffee, Milk, Pickle. Introduction. Association rules were originally designed for finding multi-correlated items in transactions. However, they can be easily adapted for classification... How ?. Example. {SL=L,. SW=M,PL = S, PW = M}. Risk Prediction. Gyorgy J. Simon. Dept. of Health Sciences Research. Mayo Clinic. SHARPn. Summit 2012. Outline. Introduction. Modeling Diabetes Risk. Association Rule Mining. Results. Diabetes Disease Network Reconstruction. Iris . virginica. 2. Iris . versicolor. 3. Iris . setosa. 4. 1.1 Data Mining and Machine Learning. 5. Definition of Data Mining. The process of discovering patterns in data.. (The patterns discovered must be meaningful in that they lead to some advantage, usually an economic one.). Iris . virginica. 2. Iris . versicolor. 3. Iris . setosa. 4. 1.1 Data Mining and Machine Learning. 5. Definition of Data Mining. The process of discovering patterns in data.. (The patterns discovered must be meaningful in that they lead to some advantage, usually an economic one.). 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. What Is Association Rule Mining?. Association rule mining. . is finding frequent patterns or associations among sets of items or objects, usually amongst transactional data. Applications include Market Basket analysis, cross-marketing, catalog design, etc.. Lecture Organization (Chapter 7). Coping with Categorical and Continuous . Attributes . shortened version in 2015. Multi-Level Association Rules . skipped in . 2015. Sequence Mining . © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 . Frequent Itemset Mining & Association Rules Mining of Massive Datasets Jure Leskovec, Anand Rajaraman , Jeff Ullman Stanford University http://www.mmds.org Note to other teachers and users of these Global . and Local Association Rules. Abhishek Mukherji*. . Elke . A. . . Rundensteiner Matthew . O. . Ward. Department of Computer Science, Worcester Polytechnic Institute, MA, USA.