Relevant bibliographies by topics / Clickstream

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Clickstream'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Clickstream"

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Makhecha, Harshit, Dharmendra Singh, Bhagirath Prajapati, and Priyanka Puvar. "Clickstream Analysis using Hadoop." International Journal of Computer Trends and Technology 34, no. 2 (April 25, 2016): 89–92. http://dx.doi.org/10.14445/22312803/ijctt-v34p115.

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Wang, Gang, Xinyi Zhang, Shiliang Tang, Christo Wilson, Haitao Zheng, and Ben Y. Zhao. "Clickstream User Behavior Models." ACM Transactions on the Web 11, no. 4 (September 2017): 1–37. http://dx.doi.org/10.1145/3068332.

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HU, JIA, and NING ZHONG. "WEB FARMING WITH CLICKSTREAM." International Journal of Information Technology & Decision Making 07, no. 02 (June 2008): 291–308. http://dx.doi.org/10.1142/s0219622008002971.

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In a commercial website or portal, Web information fusion is usually from the following two approaches, one is to integrate the Web content, structure, and usage data for surfing behavior analysis; the other is to integrate Web usage data with traditional customer, product, and transaction data for purchasing behavior analysis. In this paper, we propose a unified model based on Web farming technology for collecting clickstream logs in the whole user interaction process. We emphasize that collecting clickstream logs at the application layer will help to seamlessly integrate Web usage data with other customer-related data sources. In this paper, we extend the Web log standard to modeling clickstream format and Web mining to Web farming from passively collecting data and analyzing the customer behavior to actively influence the customer's decision making. The proposed model can be developed as a common plugin for most existing commercial websites and portals.
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Zhang, Guang Qian, and Xue Bai. "Character Analysis of Online Consumers Based on Bayesian Network." Applied Mechanics and Materials 411-414 (September 2013): 2235–40. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.2235.

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Clickstream data provide information for the behavior traces of online consumers. These traces contain the user's personality psychology. In this paper, clickstream data is researched to analyze the online consumer character in personality psychology. We propose a consumer character analysis method by Bayesian network. By using some variables at the session level, we construct the Bayesian network and dig out the consumer character. Then, with some real clickstream data, this method is applied to an online shopper. Finally, we get the dominant type of consumer character and related variables. Through interview with user, the validity of this method is verified.
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HengLi Yang, and QingFeng Lin. "Clickstream Analysis on WMC Platform." Journal of Convergence Information Technology 6, no. 1 (January 31, 2011): 212–17. http://dx.doi.org/10.4156/jcit.vol6.issue1.25.

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Antonellis, Panagiotis, Christos Makris, and Nikos Tsirakis. "Algorithms for clustering clickstream data." Information Processing Letters 109, no. 8 (March 2009): 381–85. http://dx.doi.org/10.1016/j.ipl.2008.12.011.

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Dextras‐Romagnino, K., and T. Munzner. "Segmentifier: Interactive Refinement of Clickstream Data." Computer Graphics Forum 38, no. 3 (June 2019): 623–34. http://dx.doi.org/10.1111/cgf.13715.

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Goldfarb, Avi, and Qiang Lu. "Household-Specific Regressions Using Clickstream Data." Statistical Science 21, no. 2 (May 2006): 247–55. http://dx.doi.org/10.1214/088342306000000150.

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Abdoli, Mansour, Paul Savory, and F. Fred Choobineh. "Framework for simulating random clickstream data." International Journal of Electronic Marketing and Retailing 5, no. 1 (2012): 63. http://dx.doi.org/10.1504/ijemr.2012.047594.

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Cotter, Scott. "Web Analysis from Clickstream to Customer." Handbook of Business Strategy 4, no. 1 (January 2003): 389–94. http://dx.doi.org/10.1108/eb060294.

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Dissertations / Theses on the topic "Clickstream"

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Kliegr, Tomáš. "Clickstream Analysis." Master's thesis, Vysoká škola ekonomická v Praze, 2007. http://www.nusl.cz/ntk/nusl-2065.

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Thesis introduces current research trends in clickstream analysis and proposes a new heuristic that could be used for dimensionality reduction of semantically enriched data in Web Usage Mining (WUM). Click-fraud and conversion fraud are identified as key prospective application areas for WUM. Thesis documents a conversion fraud vulnerability of Google Analytics and proposes defense - a new clickstream acquisition software, which collects data in sufficient granularity and structure to allow for data mining approaches to fraud detection. Three variants of K-means clustering algorithms and three association rule data mining systems are evaluated and compared on real-world web usage data.
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Jamalzadeh, Mohammadamin. "Analysis of clickstream data." Thesis, Durham University, 2011. http://etheses.dur.ac.uk/3366/.

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This thesis is concerned with providing further statistical development in the area of web usage analysis to explore web browsing behaviour patterns. We received two data sources: web log files and operational data files for the websites, which contained information on online purchases. There are many research question regarding web browsing behaviour. Specifically, we focused on the depth-of-visit metric and implemented an exploratory analysis of this feature using clickstream data. Due to the large volume of data available in this context, we chose to present effect size measures along with all statistical analysis of data. We introduced two new robust measures of effect size for two-sample comparison studies for Non-normal situations, specifically where the difference of two populations is due to the shape parameter. The proposed effect sizes perform adequately for non-normal data, as well as when two distributions differ from shape parameters. We will focus on conversion analysis, to investigate the causal relationship between the general clickstream information and online purchasing using a logistic regression approach. The aim is to find a classifier by assigning the probability of the event of online shopping in an e-commerce website. We also develop the application of a mixture of hidden Markov models (MixHMM) to model web browsing behaviour using sequences of web pages viewed by users of an e-commerce website. The mixture of hidden Markov model will be performed in the Bayesian context using Gibbs sampling. We address the slow mixing problem of using Gibbs sampling in high dimensional models, and use the over-relaxed Gibbs sampling, as well as forward-backward EM algorithm to obtain an adequate sample of the posterior distributions of the parameters. The MixHMM provides an advantage of clustering users based on their browsing behaviour, and also gives an automatic classification of web pages based on the probability of observing web page by visitors in the website.
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Ekberg, Fredrik. "Jämförelse av analysmetoder för clickstream-data." Thesis, University of Skövde, School of Humanities and Informatics, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-873.

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Det här arbetet har som syfte att genom en jämförelse av olika analysmetoder för clickstream-data kunna fungera som en vägledning när en metod ska implementeras. Metoden som använts vid jämförelsen är litteraturstudie i och med att de analyseringsmetoder som ska undersökas redan är framtagna och kunskap om dem fås genom att studera litteratur i vilka de förekommer. Ett antal kriterier används sedan vid själva jämförelsen, anledningen till detta är att metoderna ska jämföras utifrån en gemensam grund.

De metoder som uppfyllde kraven för de olika kriterierna bäst var page events fact model och subsession fact model. Subsession fact model kan dock upplevas som det bästa valet i alla lägen men samtidigt är den kanske lite överdriven om clickstream-datan bara ska användas till att se hur besökarna använder varje individuell sida för att användas i designsupport syfte. Det går alltså att påvisa att syftet styr vilken metod som är mest lämpad.

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Hotle, Susan Lisa. "Applications of clickstream information in estimating online user behavior." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53507.

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The internet has become a more prominent part of people’s lives. Clickstream and other online data have enabled researchers to better understand consumers’ decision-making behavior in a variety of application areas. This dissertation focuses on using clickstream data in two application areas: the airline industry and the field of education. The first study investigates if airline passengers departing from or arriving to a multi-airport city actually consider itineraries at the airports not considered to be their preferred airport. It was found that customers do consider fares at multiple airports in multi-airport cities. However, other trip characteristics, typically linked to whether a customer is considered business or leisure, were found to have a larger impact on customer behavior than offered fares at competing airports. The second study evaluates airline customer search and purchase behavior near the advance purchase deadlines, which typically signify a price increase. Search and purchase demand models were constructed using instrumented two-stage least squares (2SLS) models with valid instruments to correct for endogeneity. Increased demand was found before each deadline, even though these deadlines are not well-known among the general public. It is hypothesized that customers are able to use two methods to unintentionally book right before these price increases: (1) altering their travel dates by one or two days using the flexible dates tools offered by an airline’s or online travel agency’s (OTA) website to receive a lower fare, (2) booking when the coefficient of variation across competitor fares is high, as the dynamics of one-way and roundtrip pricing differ near these deadlines. The third study uses clickstream data in the field of education to compare the success of the traditional, flipped, and micro-flipped classrooms as well as their impacts on classroom attitudes. Students’ quiz grades were not significantly different between the traditional and flipped classrooms. The flipped classroom reduced the impact of procrastination on success. In the end, it was found that micro-flipped was most preferred by students as it incorporated several benefits of the flipped classroom without the effects of a learning curve.
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Li, Richard D. (Richard Ding) 1978. "Web clickstream data analysis using a dimensional data warehouse." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86671.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2001.
Includes bibliographical references (leaves 83-84).
by Richard D. Li.
M.Eng.
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Wong, Mark Alan. "Logging clickstream data into a database on a consolidated system /." Full text open access at:, 2002. http://content.ohsu.edu/u?/etd,274.

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Johansson, Henrik. "Using clickstream data as implicit feedback in information retrieval systems." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233870.

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This Master's thesis project aims to investigate if Wikipedia's clickstream data can be used to improve the retrieval performance of information retrieval systems. The project is conducted under the assumption that a traversal between two article connects the two articles in regards to content. To extract useful terms out of the clickstream data, it needed to be structured so that it given a Wikipedia article it is possible to find all of the in-going or out-going article traversals.The project settled on using the clickstream data in an automatic query expansion approach.Two expansion methods were investigated, one based on expanding with full article title so that the context would be preserved, and the other expanded with individual terms from the article titles.The structure of the data and two proposed methods were evaluated using a set of queries and relevance judgments. The results of the evaluation shows that the method that expands with individual terms performed better than the full article title expansion method and that the individual term method managed to increase the MAP with 11.24%.  The expansion method was evaluated on two different query collections, and it was found that the proposed expansion method only improves the results where the average recall of the original queries are low.The thesis conclusion is that the clickstream can be used to improve retrieval performance for an information retrieval system.
Det här examensarbetets mål är att undersöka om Wikipedias klickströmsdata kan användas för att förbättra sökprestanda för informationsökningssystem. Arbetet har utförts under antagandet att en övergång mellan två artiklar på Wikipedia sammankopplar artiklarnas innehåll och är av intresse för användaren. För att kunna utnyttja klickströmsdatan krävs det att den struktureras på ett användbart sätt så att det givet en artikel går att se hur läsare har förflyttat sig ut eller in mot artikeln. Vi valde att utnyttja datamängden genom en automatisk sökfrågeexpansion. Två olika metoder togs fram, där den första expanderar sökfrågan med hela artikeltitlar medans den andra expanderar med enskilda ord ur en artikeltitel.Undersökningens resultat visar att den ordbaserade expansionsmetoden presterar bättre än metoden som expanderar med hela artikeltitlar. Den ordbaserade expansionsmetoden lyckades uppnå en förbättring för måttet MAP med 11.21%. Från arbetet kan man också se att expansionmetoden enbart förbättrar prestandan när täckningen för den ursprungliga sökfrågan är liten. Gällande strukturen på klickströmsdatan så presterade den utgående strukturen bättre än den ingående. Examensarbetets slutsats är att denna klickströmsdata lämpar sig bra för att förbättra sökprestanda för ett informationsökningssystem.
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Collin, Sara, and Ingrid Möllerberg. "Designing an Interactive tool for Cluster Analysis of Clickstream Data." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-414237.

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The purpose of this study was to develop an interactive tool that enables identification of different types of users of an application based on clickstream data. A complex hierarchical clustering algorithm tool called Recursive Hierarchical Clustering (RHC) was used. RHC provides a visualisation of user types as clusters, where each cluster has its own distinguishing action pattern, i.e., one or several consecutive actions made by the user in the application. A case study was conducted on the mobile application Plick, which is an application for selling and buying second hand clothes. During the course of the project, the analysis and its result was discovered to be difficult to understand by the operators of the tool. The interactive tool had to be extended to visualise the complex analysis and its result in an intuitive way. A literature study of how humans interpret information, and how to present it to operators, was conducted and led to a redesign of the tool. More information was added to each cluster to enable further understanding of the clustering results. A clustering reconfiguration option was also created where operators of the tool got the possibility to interact with the analysis. In the reconfiguration, the operator could change the input file of the cluster analysis and thus the end result. Usability tests showed that the extra added information about the clusters served as an amplification and a verification of the original results presented by RHC. In some cases the original result presented by RHC was used as a verification to user group identification made by the operator solely based on the extra added information. The usability tests showed that the complex analysis with its results could be understood and configured without considerable comprehension of the algorithm. Instead it seemed like it could be successfully used in order to identify user types with help of visual clues in the interface and default settings in the reconfiguration. The visualisation tool is shown to be successful in identifying and visualising user groups in an intuitive way.
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Neville, Kevin. "Channel attribution modelling using clickstream data from an online store." Thesis, Linköpings universitet, Statistik och maskininlärning, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139318.

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In marketing, behaviour of users is analysed in order to discover which channels (for instance TV, Social media etc.) are important for increasing the user’s intention to buy a product. The search for better channel attribution models than the common last-click model is of major concern for the industry of marketing. In this thesis, a probabilistic model for channel attribution has been developed, and this model is demonstrated to be more data-driven than the conventional last- click model. The modelling includes an attempt to include the time aspect in the modelling which have not been done in previous research. Our model is based on studying different sequence length and computing conditional probabilities of conversion by using logistic regression models. A clickstream dataset from an online store was analysed using the proposed model. This thesis has revealed proof of that the last-click model is not optimal for conducting these kinds of analyses.
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Bača, Roman. "Sběr sémanticky obohacených clickstreamů." Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-76722.

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The aim of this thesis is to bring near to the readers the area of webmining and familiarize them with tools, which deal with data mining on the web. The main emphasis is placed on the analytical software program called Piwik. This analytical tool is compared with others nowadays available analytical tools. This thesis also aims to create a compact documentation of the software Piwik. The largest part of this documentation is devoted to the newly programmed plugin. The principle of information retrieval, based on user behavior on the web, is described from the common viewpoint and leads to more factual form of description of information retrieval using this new plugin.
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Books on the topic "Clickstream"

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Thieme, Richard. Islands in the Clickstream. San Diego: Elsevier Science & Technology, 2010.

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Moe, Wendy. Capturing evolving visit behavior in clickstream data. Cambridge, MA: Marketing Science Institute, 2001.

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Sweiger, Mark, Mark R. Madsen, Jimmy Langston, and Howard Lombard. Clickstream Data Warehousing. Wiley, 2002.

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Islands in the Clickstream. Elsevier, 2004. http://dx.doi.org/10.1016/b978-1-931836-22-7.x5000-x.

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Islands in the Clickstream: Reflections on Life in a Virtual World. Syngress, 2004.

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Book chapters on the topic "Clickstream"

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Schmidt, Hans C. "Clickstream Analytics." In Encyclopedia of Big Data, 1–2. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-32001-4_36-1.

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Inbarani, H. Hannah, and K. Thangavel. "Mining and Analysis of Clickstream Patterns." In Studies in Computational Intelligence, 3–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01091-0_1.

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Blanc, Erika, and Paolo Giudici. "Sequence Rules for Web Clickstream Analysis." In Advances in Data Mining, 1–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46131-0_1.

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Wanzeller, Cristina, and Orlando Belo. "Improving Effectiveness on Clickstream Data Mining." In Advances in Data Mining. Applications in Medicine, Web Mining, Marketing, Image and Signal Mining, 161–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11790853_13.

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Kannappady, Srinidhi, Sudhir P. Mudur, and Nematollaah Shiri. "Clickstream Visualization Based on Usage Patterns." In Computer Vision, Graphics and Image Processing, 339–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11949619_31.

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Almalki, Sultan, Prosenjit Chatterjee, and Kaushik Roy. "Continuous Authentication Using Mouse Clickstream Data Analysis." In Security, Privacy, and Anonymity in Computation, Communication, and Storage, 76–85. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24900-7_6.

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Lu, Lin, Margaret Dunham, and Yu Meng. "Mining Significant Usage Patterns from Clickstream Data." In Advances in Web Mining and Web Usage Analysis, 1–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11891321_1.

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Ahmadi-Abkenari, Fatemeh, and Ali Selamat. "Parallel Web Crawler Architecture for Clickstream Analysis." In Communications in Computer and Information Science, 123–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32826-8_13.

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Bui, Bang V., Bay Vo, Huy M. Huynh, Tu-Anh Nguyen-Hoang, and Bao Huynh. "An Efficient Method for Mining Clickstream Patterns." In Rough Sets, 572–83. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99368-3_45.

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Selamat, Ali, and Fatemeh Ahmadi-Abkenari. "Architecture for a Parallel Focused Crawler for Clickstream Analysis." In Intelligent Information and Database Systems, 27–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20039-7_3.

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Conference papers on the topic "Clickstream"

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"VizClick - Visualizing Clickstream Data." In International Conference on Information Visualization Theory and Applications. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004687102470255.

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Huynh, Huy M., Loan T. T. Nguyen, Bay Vo, Zuzana Kominkova Oplatkova, and Tzung-Pei Hong. "Mining Clickstream Patterns Using IDLists." In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2019. http://dx.doi.org/10.1109/smc.2019.8914086.

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"MINING CLICKSTREAM-BASED DATA CUBES." In 6th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2004. http://dx.doi.org/10.5220/0002631305830586.

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Speiser, Michel, Gianluca Antonini, Abderrahim Labbi, and Juliana Sutanto. "On nested palindromes in clickstream data." In the 18th ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2339530.2339758.

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Wei, Jishang, Zeqian Shen, Neel Sundaresan, and Kwan-Liu Ma. "Visual cluster exploration of web clickstream data." In 2012 IEEE Conference on Visual Analytics Science and Technology (VAST). IEEE, 2012. http://dx.doi.org/10.1109/vast.2012.6400494.

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Lakshminarayan, Choudur, Ram Kosuru, and Meichun Hsu. "Modeling Complex Clickstream Data by Stochastic Models." In the 25th International Conference Companion. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/2872518.2891070.

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Hanamanthrao, Ramanna, and S. Thejaswini. "Real-time clickstream data analytics and visualization." In 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). IEEE, 2017. http://dx.doi.org/10.1109/rteict.2017.8256978.

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Weller, Tobias. "Compromised Account Detection Based on Clickstream Data." In Companion of the The Web Conference 2018. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3184558.3186569.

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Kim, Dong-Ho, Vijayalakshmi Atluri, Michael Bieber, Nabil Adam, and Yelena Yesha. "A clickstream-based collaborative filtering personalization model." In the 6th annual ACM international workshop. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1031453.1031470.

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Wang, Yun, Zhutian Chen, Quan Li, Xiaojuan Ma, Qiong Luo, and Huamin Qu. "Animated narrative visualization for video clickstream data." In SA '16: SIGGRAPH Asia 2016. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/3002151.3002155.

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Reports on the topic "Clickstream"

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Young, Charlotte, Myriam Abramson, and Steve Russell. Data Acquisition from tcpdump: Recovering and Reconstituting Clickstream Data. Fort Belvoir, VA: Defense Technical Information Center, August 2012. http://dx.doi.org/10.21236/ada603295.

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