TAILIEUCHUNG - Mining Association Rules between Sets of Items in Large Databases

Query processing takes place on a database front-end while signal-processing functions are executed on the sensor nodes involved in the query. The query execution engine on the database front-end includes a mechanism for interacting with remote sensors. On each sensor a lightweight query execution engine is responsible for executing signal processing functions and sending data back to the front-end. In COUGAR, we assume that there. | Mining Association Rules between Sets of Items in Large Databases Rakesh Agrawal Tomasz Imielinski Arun Swami IBM Almaden Research Center 650 Harry Road San Jose CA 95120 Abstract We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company which shows the effectiveness of the algorithm. 1 Introduction Consider a supermarket with a large collection of items. Typical business decisions that the management of the supermarket has to make include what to put on sale how to design coupons how to place merchandise on shelves in order to maximize the profit etc. Analysis of past transaction data is a commonly used approach in order to improve the quality of such decisions. Until recently however only global data about the cumulative sales during some time period a day a week a month etc. was available on the computer. Progress in bar-code technology has made it possible to store the so called basket data that stores items purchased on a per-transaction basis. Basket data type transactions do not necessarily consist of items bought together at the same point of time. It may consist of items bought by a customer over a period of time. Examples include monthly purchases by members of a book club or a music club. Current address Computer Science Department Rutgers University New Brunswick NJ 08903 Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage the ACM copyright notice and the title of the publication and its date appear and notice is given that copying is by permission of the Association for Computing

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