ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Pattern-growth based frequent serial episode discovery

Achar, Avinash and Ibrahim, A and Sastry, PS (2013) Pattern-growth based frequent serial episode discovery. In: DATA & KNOWLEDGE ENGINEERING, 87 . pp. 91-108.

[img] PDF
Dat_kno_eng_87_91_2013.pdf - Published Version
Restricted to Registered users only

Download (1562Kb) | Request a copy
Official URL: http://dx.doi.org/10.1016/j.datak.2013.06.005

Abstract

Frequent episode discovery is a popular framework for pattern discovery from sequential data. It has found many applications in domains like alarm management in telecommunication networks, fault analysis in the manufacturing plants, predicting user behavior in web click streams and so on. In this paper, we address the discovery of serial episodes. In the episodes context, there have been multiple ways to quantify the frequency of an episode. Most of the current algorithms for episode discovery under various frequencies are apriori-based level-wise methods. These methods essentially perform a breadth-first search of the pattern space. However currently there are no depth-first based methods of pattern discovery in the frequent episode framework under many of the frequency definitions. In this paper, we try to bridge this gap. We provide new depth-first based algorithms for serial episode discovery under non-overlapped and total frequencies. Under non-overlapped frequency, we present algorithms that can take care of span constraint and gap constraint on episode occurrences. Under total frequency we present an algorithm that can handle span constraint. We provide proofs of correctness for the proposed algorithms. We demonstrate the effectiveness of the proposed algorithms by extensive simulations. We also give detailed run-time comparisons with the existing apriori-based methods and illustrate scenarios under which the proposed pattern-growth algorithms perform better than their apriori counterparts. (C) 2013 Elsevier B.V. All rights reserved.

Item Type: Journal Article
Related URLs:
Additional Information: Copyright for this article belongs to Elsevier Science
Keywords: Mining methods and algorithms; Frequent episodes; Non-overlapped frequency; Span constraints; Gap constraints; Depth-first search
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 16 Dec 2013 06:48
Last Modified: 16 Dec 2013 06:48
URI: http://eprints.iisc.ernet.in/id/eprint/47901

Actions (login required)

View Item View Item