In April 2016 Manchester eScholar was replaced by the University of Manchester’s new Research Information Management System, Pure. In the autumn the University’s research outputs will be available to search and browse via a new Research Portal. Until then the University’s full publication record can be accessed via a temporary portal and the old eScholar content is available to search and browse via this archive.

Discovery of events with negative behavior against given sequential patterns

Intelligent Systems (IS), 2010 5th IEEE International Conference;2010.

Access to files

Full-text and supplementary files are not available from Manchester eScholar. Use our list of Related resources to find this item elsewhere. Alternatively, request a copy from the Library's Document supply service.

Abstract

The dramatic drop in the prices of data collection and storage devices has not only enabled organisations to store almost every activity of their business processes, they can also retain every state of these activities as well. Availability of these masses of data also means that by implementing different data mining techniques we can yield more accurate and useful information to be used for important decision making. One of the key mining techniques on such data is to discover sequential patterns. Most of the existing sequential pattern mining approaches mainly deal with finding the positive behaviour of a sequential pattern that can help in predicting the next event after a sequence of events. In this paper we propose the concept of Negative Behaviour Against the Sequential Pattern (NBASP) that is to discover the events/event-sets which are unlikely to follow the given sequential pattern and discuss its applications in a variety of domains. A comprehensive problem definition and efficient algorithm to discover NBASP is presented.

Bibliographic metadata

Type of resource:
Content type:
Publication date:
Abstract:
The dramatic drop in the prices of data collection and storage devices has not only enabled organisations to store almost every activity of their business processes, they can also retain every state of these activities as well. Availability of these masses of data also means that by implementing different data mining techniques we can yield more accurate and useful information to be used for important decision making. One of the key mining techniques on such data is to discover sequential patterns. Most of the existing sequential pattern mining approaches mainly deal with finding the positive behaviour of a sequential pattern that can help in predicting the next event after a sequence of events. In this paper we propose the concept of Negative Behaviour Against the Sequential Pattern (NBASP) that is to discover the events/event-sets which are unlikely to follow the given sequential pattern and discuss its applications in a variety of domains. A comprehensive problem definition and efficient algorithm to discover NBASP is presented.

Institutional metadata

University researcher(s):

Record metadata

Manchester eScholar ID:
uk-ac-man-scw:136227
Created by:
Anwar, Fahad
Created:
11th November, 2011, 15:57:44
Last modified by:
Anwar, Fahad
Last modified:
11th December, 2014, 19:11:59

Can we help?

The library chat service will be available from 11am-3pm Monday to Friday (excluding Bank Holidays). You can also email your enquiry to us.