Data Preparation for Mining World Wide Web Browsing Patterns

Cooley, Robert; Mobasher, Bamshad; Srivastava, Jaideep

doi:10.1007/bf03325089

Cited by 1,038 publications

(623 citation statements)

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“…Data preparation is important for data mining [10]- [12]. Before we describe the data preparation procedure, we describe the log data used in this paper.…”

Section: Data Preparationmentioning

confidence: 99%

Smartphone App Usage Log Mining

Tseng¹,

Hsu²

2014

IJCEE

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Abstract-The rise of smartphones can be connected to the large number of applications, or apps, which can be installed and run by users on smartphones. It becomes important for researchers, smartphone designers, and application developers to know how users use apps on their smartphones. The aim of this paper is to present our work where data mining is applied to smartphone app usage log data with a focus on data preparation. The methods include association rule mining and sequential pattern mining. The results are the discovered rules and patterns that reflect users' real-life app-using behaviors; they can lead to improved user interfaces, and they can also be used to reconstruct the context in which a user used his or her smartphone. We demonstrate an application of data mining that can have an impact on the smartphone industry, and we also demonstrate a data-driven approach that can help us know more about how users use apps on their smartphones.Index Terms-Association rule mining, sequential pattern mining, smartphone app usage log.

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“…Data preparation is important for data mining [10]- [12]. Before we describe the data preparation procedure, we describe the log data used in this paper.…”

Section: Data Preparationmentioning

confidence: 99%

Smartphone App Usage Log Mining

Tseng¹,

Hsu²

2014

IJCEE

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“…Various data preparation techniques have be described in [CMS99]. Three transaction identification approaches are compared in this study.…”

Section: Related Workmentioning

confidence: 99%

“…Note that our notion of IP transaction is often referred to as user transaction in previous literature (e.g., [CMS99]). However, as we have shown that an IP sequence can actually contain requests from multiple users, an IP transaction may also be composed of page references from multiple users.…”

Section: Definition 2 (Ip Transaction) An Ip Transaction Denoted Bymentioning

confidence: 99%

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Cut-and-Pick Transactions for Proxy Log Mining

Lou

Liu

Lü

et al. 2002

Advances in Database Technology — EDBT 2002

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Abstract. Web logs collected by proxy servers, referred to as proxy logs or proxy traces, contain information about Web document accesses by many users against many Web sites. This "many-to-many" characteristic poses a challenge to Web log mining techniques due to the difficulty in identifying individual access transactions. This is because in a proxy log, user transactions are not clearly bounded and are sometimes interleaved with each other as well as with noise. Most previous work has used simplistic measures such as a fixed time interval as a determination method for the transaction boundaries, and has not addressed the problem of interleaving and noisy transactions. In this paper, we show that this simplistic view can lead to poor performance in building models to predict future access patterns. We present a more advanced cut-and-pick method for determining the access transactions from proxy logs, by deciding on more reasonable transaction boundaries and by removing noisy accesses. Our method takes advantage of the user behavior that in most transactions, the same user typically visits multiple, related Web sites that form clusters. These clusters can be discovered by our algorithm based on the connectivity among Web sites. By using real-world proxy logs, we experimentally show that this cut-and-pick method can produce more accurate transactions that result in Web-access prediction models with higher accuracy.

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“…People are buying more and more over the Internet instead of going traditional shopping. E-commerce provides customers with the opportunity of browsing endless product catalogues, comparing prices, being continuously informed, creating wish list and enjoying a better service based on their individual interests [1]- [4]. The growing electronic market is highly aggressive, featuring the possibility for a customer to easily move from one e-commerce when their necessities are not satisfied.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2018

IJET

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Data Preparation for Mining World Wide Web Browsing Patterns

Cited by 1,038 publications

References 4 publications

Smartphone App Usage Log Mining

Smartphone App Usage Log Mining

Cut-and-Pick Transactions for Proxy Log Mining

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