Position Coded Pre-order Linked WAP-Tree for Web Log Sequential Pattern Mining

Lu, Yi; Ezeife, C. I.

doi:10.1007/3-540-36175-8_33

Cited by 18 publications

(20 citation statements)

References 4 publications

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“…Usually, a proposed algorithm also has a proposed data structure to accompany it, such as SPADE [Zaki 1998] with vertical databases; PrefixSpan [Pei at al. 2001] with projected databases; WAP-Mine ] with WAP-tree; and PLWAP [Lu and Ezeife 2003] with its PLWAP-tree.…”

Section: A Taxonomy Of Sequential Pattern-mining Algorithmsmentioning

confidence: 99%

“…(1) Sampling and/or compression: Compression is mainly used in pattern-growth tree projection methods, such as FS-Miner [El-Sayed et al 2004]; WAP-tree ; PLWAP [Lu and Ezeife 2003]; and PS-tree (a progressive sequential-mining algorithm [Huang et al 2006]). In the apriori-based category, compression is used to solve the problem of fast memory consumption due to the explosive growth of candidate sequences by some apriori-based algorithms, like Apriori-GST [Tanasa 2005].…”

Section: Seven More Features In the Taxonomy For Pattern-growth Algormentioning

confidence: 99%

“…Pattern-growth algorithms, and later early-pruning algorithms, try to utilize a data structure that allows them to prune candidate sequences early in the mining process (e.g., WAP-mine ; PLWAP [Lu and Ezeife 2003]; and PrefixSpan [Pei et al 2001]), while earlypruning algorithms try to predict candidate sequences in an effort to look-ahead in order to avoid generating them (e.g., LAPIN [Yang et al 2005]; DISC-all [Chiu et al 2004]), as discussed in Section 3.3.…”

Section: Seven More Features In the Taxonomy For Pattern-growth Algormentioning

confidence: 99%

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A taxonomy of sequential pattern mining algorithms

2010

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Owing to important applications such as mining web page traversal sequences, many algorithms have been introduced in the area of sequential pattern mining over the last decade, most of which have also been modified to support concise representations like closed, maximal, incremental or hierarchical sequences. This article presents a taxonomy of sequential pattern-mining techniques in the literature with web usage mining as an application. This article investigates these algorithms by introducing a taxonomy for classifying sequential pattern-mining algorithms based on important key features supported by the techniques. This classification aims at enhancing understanding of sequential pattern-mining problems, current status of provided solutions, and direction of research in this area. This article also attempts to provide a comparative performance analysis of many of the key techniques and discusses theoretical aspects of the categories in the taxonomy.

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Section: A Taxonomy Of Sequential Pattern-mining Algorithmsmentioning

confidence: 99%

Section: Seven More Features In the Taxonomy For Pattern-growth Algormentioning

confidence: 99%

Section: Seven More Features In the Taxonomy For Pattern-growth Algormentioning

confidence: 99%

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A taxonomy of sequential pattern mining algorithms

2010

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“…The algorithms were implemented in Java, and run in the JBuilder 8 personal edition. Synthetic datasets were generated using the publicly available data generation program from the IBM Quest data-mining project 1 , which has been used in several web-usage mining studies [Pei et al 2000;Lu and Ezeife 2003]. The following parameters are used to generate the datasets: |D| -number of sequences in the database; |C| -average length of the sequences; |S| -average length of potentially frequent sequences; and |N| -number of objects in the database.…”

Section: Discovering Navigational Paths Of Users Without Pre-defined mentioning

confidence: 99%

“…We compare the performance of our technique to the apriori-based technique for discovering contiguous navigational patterns [CPY98]. (Note that the tree-based algorithms for discovering navigational patterns [Pei et al 2000;Lu and Ezeife 2003] cannot differentiate contiguous navigational patterns, thus, we do not include them in our evaluations.) The results of our experiments are reported in Table II. 1 http://www.almaden.ibm.com/cs/quest We also tested the algorithms with publicly available, anonymous, real-world web logs of "msnbc.com" users [Hettich and Bay 1999].…”

Section: Discovering Navigational Paths Of Users Without Pre-defined mentioning

confidence: 99%

A framework for representing navigational patterns as full temporal objects

2004

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Navigational patterns have applications in several areas including: web personalization, recommendation, userprofiling and clustering, etc. Most existing works on navigational pattern-discovery give little consideration to the effects of time (or temporal trends) on navigational patterns. Some recent works have proposed frameworks for partial temporal representation of navigational patterns. This paper proposes a framework that models navigational patterns as full temporal objects that may be represented as time series. Such a representation allows a rich array of analysis techniques to be applied to the data. The proposed framework also enhances the understanding and interpretation of discovered patterns, and provides a rich environment for integrating the analysis of navigational patterns with data from the underlying organizational environments and other external factors. Such integrated analysis is very helpful in understanding navigational patterns (e.g., E-commerce sites may integrate the trend analysis of navigational patterns with other market data and economic indicators). To achieve full temporal representation, this paper proposes a navigational pattern-discovery technique that is not based on pre-defined thresholds. This is a shift from existing techniques that are driven by pre-defined thresholds that can only support partial temporal representation of navigational patterns.

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