Heavy path based super-sequence frequent pattern mining on web log dataset

Yu, Xinran; Korkmaz, Turgay

doi:10.5430/air.v4n2p1

Cited by 4 publications

(4 citation statements)

References 35 publications

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“…W-support (weight support) measurement (Sun and Bai, 2008) is not recommended for dense data sets as well. In sequential pattern mining, researchers face pattern growth problem (Yu and Korkmaz, 2015). In sequential mining, huge numbers of patterns are detected and the length of the patterns is also large.…”

Section: Web Session Mining Techniquesmentioning

confidence: 99%

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An Episode-based Approach to Identify Website User Access Patterns

Udantha

Ranathunga

Dias

2016

Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods

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Mining web access log data is a popular technique to identify frequent access patterns of website users. There are many mining techniques such as clustering, sequential pattern mining and association rule mining to identify these frequent access patterns. Each can find interesting access patterns and group the users, but they cannot identify the slight differences between accesses patterns included in individual clusters. But in reality these could refer to important information about attacks. This paper introduces a methodology to identify these access patterns at a much lower level than what is provided by traditional clustering techniques, such as nearest neighbour based techniques and classification techniques. This technique makes use of the concept of episodes to represent web sessions. These episodes are expressed in the form of regular expressions. To the best of our knowledge, this is the first time to apply the concept of regular expressions to identify user access patterns in web server log data. In addition to identifying frequent patterns, we demonstrate that this technique is able to identify access patterns that occur rarely, which would have been simply treated as noise in traditional clustering mechanisms.

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Section: Web Session Mining Techniquesmentioning

confidence: 99%

“…However, this technique has not been implemented. (Yu and Korkmaz, 2015) were able to identify general structures spanning over multiple sequences by detecting the most common subsequences (e.g., the pages visited). They are not able to handle repeating (looping) sequences.…”

Section: Episodes In Sequences and Regular Expressionsmentioning

confidence: 99%

An Episode-based Approach to Identify Website User Access Patterns

Udantha

Ranathunga

Dias

2016

Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods

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“…Mining frequent itemsets from databases is an important data mining task. It has many practical applications including document clustering [15,40], social network analysis [23,34], market basked analysis [17], fraud detection [14], bioinformatics [13,28,33], mining patterns from web logs [22,38]. The concept of mining frequent itemsets and generating association rules form the frequent itemsets was proposed by [1].…”

Section: Introductionmentioning

confidence: 99%

Mining Approximate Frequent Itemsets Using Pattern Growth Approach

Bashir

Lai

2021

ITC

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Approximate frequent itemsets (AFI) mining from noisy databases are computationally more expensive than traditional frequent itemset mining. This is because the AFI mining algorithms generate large number of candidate itemsets. This article proposes an algorithm to mine AFIs using pattern growth approach. The major contribution of the proposed approach is it mines core patterns and examines approximate conditions of candidate AFIs directly with single phase and two full scans of database. Related algorithms apply Apriori-based candidate generation and test approach and require multiple phases to obtain complete AFIs. First phase generates core patterns, and second phase examines approximate conditions of core patterns. Specifically, the article proposes novel techniques that how to map transactions on approximate FP-tree, and how to mine AFIs from the conditional patterns of approximate FP-tree. The approximate FP-tree maps transactions on shared branches when the transactions share a similar set of items. This reduces the size of databases and helps to efficiently compute the approximate conditions of candidate itemsets. We compare the performance of our algorithm with the state of the art AFI mining algorithms on benchmark databases. The experiments are analyzed by comparing the processing time of algorithms and scalability of algorithms on varying database size and transaction length. The results show pattern growth approach mines AFIs in less processing time than related Apriori-based algorithms.

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“…Mining frequent itemsets from transactional databases play an important role in many data mining applications, e.g., social network mining ( Jiang, Leung, & Zhang, 2016;Moosavi, Jalali, Misaghian, Shamshirband, & Anisi, 2017 ), finding gene expression patterns ( Becquet, Blachon, Jeudy, Boulicaut, & Gandrillon, 2001;Creighton & Hanash, 2003;Cremaschi et al, 2015;Mallik, Mukhopadhyay, & Maulik, 2015 ), web log pattern mining ( Diwakar Tripathia & Edlaa, 2017;Han, Cheng, Xin, & Yan, 2007;Iváncsy, Renáta, & Vajk, 2006;Yu & Korkmaz, 2015 ). In recent years, many algorithms have been proposed for efficient mining of frequent itemsets ( Apiletti et al, 2017;Bodon, 2003;Burdick, Calimlim, Flannick, Gehrke, & Yiu, 2005;Gan, Lin, Fournier-Viger, Chao, & Zhan, 2017;Han, Pei, & Yin, 20 0 0;Kosters & Pijls, 2003;Liu, Lu, Yu, Wang, & Xiao, 2003;Pei, Tung, & Han, 2001;Uno, Kiyomi, & Arimura, 2004;Vo, Pham, Le, & Deng, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

An efficient pattern growth approach for mining fault tolerant frequent itemsets

Bashir

2020

Expert Systems with Applications

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a b s t r a c tMining fault tolerant (FT) frequent itemsets from transactional databases are computationally more expensive than mining exact matching frequent itemsets. Previous algorithms mine FT frequent itemsets using Apriori heuristic. Apriori-like algorithms generate exponential number of candidate itemsets including the itemsets that do not exist in the database. These algorithms require multiple scans of database for counting the support of candidate FT itemsets. In this paper we present a novel algorithm, which mines FT frequent itemsets using frequent pattern growth approach (FT-PatternGrowth). FT-PatternGrowth adopts a divide-and-conquer technique and recursively projects transactional database into a set of smaller projected transactional databases and mines FT frequent itemsets in each projected database by exploring only locally frequent items. This mines the complete set of FT frequent itemsets and substantially reduces those candidate itemsets that do not exist in the database. FT-PatternGrowth stores the transactional database in a highly condensed much smaller data structure called frequent pattern tree (FP-tree). The support of candidate itemsets are counted directly from the FP-tree without scanning the original database multiple times. This improves the processing speed of algorithm. Our experiments on benchmark databases indicates mining FT frequent itemsets using FT-PatternGrowth is highly efficient than Apriori-like algorithms.

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Heavy path based super-sequence frequent pattern mining on web log dataset

Cited by 4 publications

References 35 publications

An Episode-based Approach to Identify Website User Access Patterns

An Episode-based Approach to Identify Website User Access Patterns

Mining Approximate Frequent Itemsets Using Pattern Growth Approach

An efficient pattern growth approach for mining fault tolerant frequent itemsets

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