contiguous sequential pattern (CSP) mining is an important problem with many applications. Using general sequentialpattern mining algorithms for CSP mining may lead to poor performance due to the lack of consideratio...
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ISBN:
(纸本)9781424441150
contiguous sequential pattern (CSP) mining is an important problem with many applications. Using general sequentialpattern mining algorithms for CSP mining may lead to poor performance due to the lack of consideration on the contiguous property of CSP. In this paper we present a two stage approach for CSP mining. We first detect frequent itemsets in a database, based on which we partition the CSPs into subsets and apply a special data structure, General UpDown Tree, to detect all the patterns in each subset. The General Updown Tree exploits the contiguous property of CSPs to achieve a compact representation of all the sequences that contain an item. Such compact representation enables us to apply a top down approach for CSP mining and eliminates unnecessary candidate evaluation. Experiment results show that our approach is more efficient compared to previous approaches in terms of both time and space.
In this paper the problem of contiguous Item sequentialpattern ( CISP) Mining is presented as a sequentialpattern mining problem under two constraints. First, each element in a sequence consists of only one item. Se...
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In this paper the problem of contiguous Item sequentialpattern ( CISP) Mining is presented as a sequentialpattern mining problem under two constraints. First, each element in a sequence consists of only one item. Second, items appearing in the sequences that contain a pattern must be adjacent with respect to the underlying order as they appear in the pattern. Even though the problem of CISP mining can be solved by using previous approaches on sequentialpattern mining under a general constraint description framework, this may lead to poor performance due to the large searching space. To efficiently solve this problem, a new data structure, UpDown Tree, is proposed for CISP mining. UpDown Tree based approach can greatly improve the efficiency of CISP mining in terms of both time and memory comparing to previous approaches. An extensive experimental study has shown promising results with our approach.
This paper provides a comprehensive analysis of algorithms for spatio-temporal pattern mining, which helps to select appropriate algorithms for a given Moving Objects Database. We can distinguish basic and extended co...
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ISBN:
(纸本)9780769539676
This paper provides a comprehensive analysis of algorithms for spatio-temporal pattern mining, which helps to select appropriate algorithms for a given Moving Objects Database. We can distinguish basic and extended comparing functions. The basic ones correspond to research fields related to pattern mining. The extended comparing functions extend the analysis to additional aspects. All these functions are presented in the context of pattern queries for Moving Objects Databases. In addition, comparing functions can be combined into more complex ones and used in frequent pattern mining algorithms in order to obtain various summaries of all analyzed sequences. We also present two algorithms, called VGES (Vertical GSP for ExactSearch) and SES (Spade for ExactSearch), for continuous sequentialpattern mining. In the paper different methods of searching for frequent patterns are also compared.
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