tailieunhanh - Discovery of Frequent Episodes in Event Sequences

If so, the development of a traffic management plan, application to your local government authority, local Police and/or Main Roads Department, approval and advertising may be essential well in advance of your event - at least three months prior to the event. Check with your local government authority for the requirements in your town. On lodgment of the information, Council officers will inspect the area for the proposed temporary street closure and advise the applicant if it is practical and safe to do so for the purpose of conducting the event. All costs. | Data Mining and Knowledge Discovery 1 259-289 1997 c 1997 Kluwer Academic Publishers. Manufactured in The Netherlands. Discovery of Frequent Episodes in Event Sequences HEIKKI MANNILA HANNU TOIVONEN A. INKERI VERKAMO Department of Computer Science . Box 26 FIN-00014 University of Helsinki Finland Editor Usama Fayyad Received February 26 1997 Revised July 8 1997 Accepted July 9 1997 Abstract. Sequences of events describing the behavior and actions of users or systems can be collected in several domains. An episode is a collection of events that occur relatively close to each other in a given partial order. We consider the problem of discovering frequently occurring episodes in a sequence. Once such episodes are known one can produce rules for describing or predicting the behavior of the sequence. We give efficient algorithms for the discovery of all frequent episodes from a given class of episodes and present detailed experimental results. The methods are in use in telecommunication alarm management. Keywords event sequences frequent episodes sequence analysis 1. Introduction There are important data mining and machine learning application areas where the data to be analyzed consists of a sequence of events. Examples of such data are alarms in a telecommunication network user interface actions crimes committed by a person occurrences of recurrent illnesses etc. Abstractly such data can be viewed as a sequence of events where each event has an associated time of occurrence. An example of an event sequence is represented in figure 1. Here A B C D E and F are event types . different types of alarms from a telecommunication network or different types of user actions and they have been marked on a time line. Recently interest in knowledge discovery from sequential data has increased see . Agrawal and Srikant 1995 Bettini et al. 1996 Dousson et al. 1993 Hatonenetal. 1996a .