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Mining Sequential Patterns: Generalizations and Performance Improvements (1995)
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| Venue: | RESEARCH REPORT RJ 9994, IBM ALMADEN RESEARCH |
| Citations: | 758 - 5 self |
BibTeX
@INPROCEEDINGS{Srikant95miningsequential,
author = {Ramakrishnan Srikant and Rakesh Agrawal},
title = {Mining Sequential Patterns: Generalizations and Performance Improvements},
booktitle = {RESEARCH REPORT RJ 9994, IBM ALMADEN RESEARCH},
year = {1995},
publisher = {}
}
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Abstract
The problem of mining sequential patterns was recently introduced in [3]. We are given a database of sequences, where each sequence is a list of transactions ordered by transaction-time, and each transaction is a set of items. The problem is to discover all sequential patterns with a user-specified minimum support, where the support of a pattern is the number of data-sequences that contain the pattern. An example of a sequential pattern is "5 % of customers bought `Foundation' and `Ringworld' in one transaction, followed by `Second Foundation ' in a later transaction". We generalize the problem as follows. First, we add time constraints that specify a minimum and/or maximum time period between adjacent elements in a pattern. Second, we relax the restriction that the items in an element of a sequential pattern must come from the same transaction, instead allowing the items to be present in a set of transactions whose transaction-times are within a user-specified time window. Third, given a user-defined taxonomy (is-a hierarchy) on items, we allow sequential patterns to include items across all levels of the taxonomy. We present GSP, a new algorithm that discovers these generalized sequential patterns. Empirical evaluation using synthetic and real-life data indicates that GSP is much faster than the AprioriAll algorithm presented in [3]. GSP scales linearly with the number of data-sequences, and has very good scale-up properties with respect to the average data-sequence size.
Keyphrases
sequential pattern performance improvement adjacent element maximum time period is-a hierarchy real-life data user-de ned taxonomy good scale-up property new algorithm present gsp aprioriall algorithm second foundation user-speci ed minimum support time constraint average datasequence size user-speci ed time window empirical evaluation later transaction
