MapReduce-based web mining for prediction of web-user navigation

Li, Meijing; Yu, Xiuming; Ryu, Keun Ho

doi:10.1177/0165551514544096

Cited by 19 publications

(13 citation statements)

References 13 publications

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“…When we set the number of clusters at 5, the clustering result is shown in Table 6. Then, we label the clusters by calculating the TF-IDF value of emerging patterns in each cluster according to Equation (5), and the result of TF-IDF of emerging patterns is shown in Table 7. We choose the Top-2 TF-IDF emerging patterns as the labels, and the labeled results are shown in Table 8.…”

Section: Results Of Emerging Patternsmentioning

confidence: 99%

“…Many techniques in web usage mining have been proposed [3][4][5][6][7][8], and this field is still a hot topic for research in data mining. Most existing web mining techniques are performed based on association rule mining or frequent pattern mining, and these methods aim to find relationships among web pages or predict the behavior of web users.…”

Section: Introductionmentioning

confidence: 99%

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Emerging Pattern-Based Clustering of Web Users Utilizing a Simple Page-Linked Graph

Kim

et al. 2016

Sustainability

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Abstract:Web usage mining is a popular research area in data mining. With the extensive use of the Internet, it is essential to learn about the favorite web pages of its users and to cluster web users in order to understand the structural patterns of their usage behavior. In this paper, we propose an efficient approach to determining favorite web pages by generating large web pages, and emerging patterns of generated simple page-linked graphs. We identify the favorite web pages of each user by eliminating noise due to overall popular pages, and by clustering web users according to the generated emerging patterns. Afterwards, we label the clusters by using Term Frequency-Inverse Document Frequency (TF-IDF). In the experiments, we evaluate the parameters used in our proposed approach, discuss the effect of the parameters on generating emerging patterns, and analyze the results from clustering web users. The results of the experiments prove that the exact patterns generated in the emerging-pattern step eliminate the need to consider noise pages, and consequently, this step can improve the efficiency of subsequent mining tasks. Our proposed approach is capable of clustering web users from web log data.

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Section: Results Of Emerging Patternsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Emerging Pattern-Based Clustering of Web Users Utilizing a Simple Page-Linked Graph

Kim

et al. 2016

Sustainability

Self Cite

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“…Though, clustering accuracy of sequential pattern mining was not considered. MapReducebased web mining was developed in [13] to predict navigation patterns of web users. With the aid of frequent-sequencepattern-mining algorithms, web users are designed using the programming model of MapReduce.…”

Section: Related Workmentioning

confidence: 99%

Hilbert Space Clustering based Chronological Backward Search for Effective Web Sequential Pattern Mining

Prabhu¹,

Chandran²

2017

IJCA

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Web data mining is an important research topic because it attains a significant amount of interest from both academic and industrial environments. Web sequential pattern mining is an imperative for analyzing the access behavior of web users. Recently, few research works have been designed for mining the web sequential patterns. However, performance of existing techniques was not effectual. In order to overcome such limitation, Hilbert Space clustering based Chronological Backward Search (HSC-CBS) Technique is proposed. HSC-CBS Technique is designed in order to improve the performance of web sequential patterns mining. The HSC-CBS Technique at first used Hilbert space clustering in order to group the similar user's interest web patterns in web log database which resulting in improved clustering accuracy. The clustering of frequent web patterns in web log database helps for minimizing the space and time complexity of web sequential pattern mining. After clustering, HSC-CBS Technique applied chronological backward search algorithm in order to efficiently mine the web sequential patterns and improving true positive rate of web sequential pattern mining. The HSC-CBS Technique conducts the experimental works on the parameters such as execution time, space complexity, clustering accuracy, true positive rate of mining and scalability. The experimental results show that the HSC-CBS Technique is able to improve the true positive rate of pattern mining and also reduces the execution time as compared to state of the art works. KeywordsChronological backward search, Hilbert Space clustering, mining, web user, web log database, web pattern.

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“…Association rule mining is an iterative and interactive process used to discover significant, novel, and interesting rules from a database (Li et al, 2014). The discovered rules are used to identify relationships among items with diverse applications.…”

Section: Introductionmentioning

confidence: 99%

Comparative Performance of Interestingness Measures to Identify Redundant and Non-informative Rules from Web Usage Data

Sisodia¹,

Singhal²,

Kandal³

2018

IJTech

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Association rules are used to predict frequent web user behaviors from web usage data. These rules are formed using frequent items. The number of association rules increases as the number of frequent items increases and produces several redundant and non-informative rules. In this paper, five interestingness measures, including cosine, lift, leverage, confidence, and conviction with a constant value of support are compared based on the number of redundant and noninformative rules that they produce. Redundant and non-informative rules are a subset of rules present in the top generated rules. The experimental results suggested that leverage produced the least number of redundant rules in the top rules but also produced the least informative rules among all measures. Lift showed the highest number of redundant rules but the most informative rules among all the measures.

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MapReduce-based web mining for prediction of web-user navigation

Cited by 19 publications

References 13 publications

Emerging Pattern-Based Clustering of Web Users Utilizing a Simple Page-Linked Graph

Emerging Pattern-Based Clustering of Web Users Utilizing a Simple Page-Linked Graph

Hilbert Space Clustering based Chronological Backward Search for Effective Web Sequential Pattern Mining

Comparative Performance of Interestingness Measures to Identify Redundant and Non-informative Rules from Web Usage Data

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