The Experts below are selected from a list of 252 Experts worldwide ranked by ideXlab platform
John G Hughes - One of the best experts on this subject based on the ideXlab platform.
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WEBKDD - User-Driven Navigation Pattern Discovery from Internet Data
Web Usage Analysis and User Profiling, 2000Co-Authors: Matthias Baumgarten, Alex G Buchner, Sarabjot Singh Anand, Maurice Mulvenna, John G HughesAbstract:Managers of electronic commerce sites need to learn as much as possible about their customers and those browsing their virtual premises, in order to maximise the return on marketing expenditure. The discovery of marketing related Navigation Patterns requires the development of data mining algorithms capable of the discovery of sequential access Patterns from web logs. This paper introduces a new algorithm called MiDAS that extends traditional sequence discovery with a wide range of web-specific features. Domain knowledge is described as flexible Navigation templates that can specify generic Navigational behaviour of interest, network structures for the capture of web site topologies, concept hierarchies and syntactic constraints. Unlike existing approaches MiDAS supports sequence discovery from multidimensional data, which allows the detection of sequences across monitored attributes, such as URLs and http referrers. Three methods for pruning the sequences, resulting in three different types of Navigational behaviour are presented. The experimental evaluation has shown promising results in terms of functionality as well as scalability.
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user driven Navigation Pattern discovery from internet data
Lecture Notes in Computer Science, 2000Co-Authors: Matthias Baumgarten, Alex G Buchner, Sarabjot Singh Anand, Maurice Mulvenna, John G HughesAbstract:Managers of electronic commerce sites need to learn as much as possible about their customers and those browsing their virtual premises, in order to maximise the return on marketing expenditure. The discovery of marketing related Navigation Patterns requires the development of data mining algorithms capable of the discovery of sequential access Patterns from web logs. This paper introduces a new algorithm called MiDAS that extends traditional sequence discovery with a wide range of web-specific features. Domain knowledge is described as flexible Navigation templates that can specify generic Navigational behaviour of interest, network structures for the capture of web site topologies, concept hierarchies and syntactic constraints. Unlike existing approaches MiDAS supports sequence discovery from multidimensional data, which allows the detection of sequences across monitored attributes, such as URLs and http referrers. Three methods for pruning the sequences, resulting in three different types of Navigational behaviour are presented. The experimental evaluation has shown promising results in terms of functionality as well as scalability.
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Navigation Pattern discovery from internet data
ACM Workshop on Web Usage Analysis and User Profiling (WebKDD), 1999Co-Authors: Alex G Buchner, Matthias Baumgarten, Sarabjot Singh Anand, Maurice Mulvenna, John G HughesAbstract:Electronic commerce sites need to learn as much as possible about their customers and those browsing their virtual premises, in order to maximize their marketing effort. The discovery of marketing related Navigation Patterns requires the development of data mining algorithms capable of discovering sequential access Patterns from web logs. This paper introduces a new algorithm called MiDAS that extends traditional sequence discovery with a wide range of web-specific features. Domain knowledge is described as flexible Navigation templates that can specify Navigational behavior, as network structures for the capture of web site topologies, in addition to concept hierarchies and syntactic constraints. Unlike existing approaches, field dependency has been implemented, which allows the detection of sequences across monitored attributes, such as URLs and http referrers. Three different types of contained-in relationships are supported, which express different types of browsing behavior. The carried out experimental evaluation have shown promising results in terms of functionality as well as scalability.
Matthias Baumgarten - One of the best experts on this subject based on the ideXlab platform.
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WEBKDD - User-Driven Navigation Pattern Discovery from Internet Data
Web Usage Analysis and User Profiling, 2000Co-Authors: Matthias Baumgarten, Alex G Buchner, Sarabjot Singh Anand, Maurice Mulvenna, John G HughesAbstract:Managers of electronic commerce sites need to learn as much as possible about their customers and those browsing their virtual premises, in order to maximise the return on marketing expenditure. The discovery of marketing related Navigation Patterns requires the development of data mining algorithms capable of the discovery of sequential access Patterns from web logs. This paper introduces a new algorithm called MiDAS that extends traditional sequence discovery with a wide range of web-specific features. Domain knowledge is described as flexible Navigation templates that can specify generic Navigational behaviour of interest, network structures for the capture of web site topologies, concept hierarchies and syntactic constraints. Unlike existing approaches MiDAS supports sequence discovery from multidimensional data, which allows the detection of sequences across monitored attributes, such as URLs and http referrers. Three methods for pruning the sequences, resulting in three different types of Navigational behaviour are presented. The experimental evaluation has shown promising results in terms of functionality as well as scalability.
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user driven Navigation Pattern discovery from internet data
Lecture Notes in Computer Science, 2000Co-Authors: Matthias Baumgarten, Alex G Buchner, Sarabjot Singh Anand, Maurice Mulvenna, John G HughesAbstract:Managers of electronic commerce sites need to learn as much as possible about their customers and those browsing their virtual premises, in order to maximise the return on marketing expenditure. The discovery of marketing related Navigation Patterns requires the development of data mining algorithms capable of the discovery of sequential access Patterns from web logs. This paper introduces a new algorithm called MiDAS that extends traditional sequence discovery with a wide range of web-specific features. Domain knowledge is described as flexible Navigation templates that can specify generic Navigational behaviour of interest, network structures for the capture of web site topologies, concept hierarchies and syntactic constraints. Unlike existing approaches MiDAS supports sequence discovery from multidimensional data, which allows the detection of sequences across monitored attributes, such as URLs and http referrers. Three methods for pruning the sequences, resulting in three different types of Navigational behaviour are presented. The experimental evaluation has shown promising results in terms of functionality as well as scalability.
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Navigation Pattern discovery from internet data
ACM Workshop on Web Usage Analysis and User Profiling (WebKDD), 1999Co-Authors: Alex G Buchner, Matthias Baumgarten, Sarabjot Singh Anand, Maurice Mulvenna, John G HughesAbstract:Electronic commerce sites need to learn as much as possible about their customers and those browsing their virtual premises, in order to maximize their marketing effort. The discovery of marketing related Navigation Patterns requires the development of data mining algorithms capable of discovering sequential access Patterns from web logs. This paper introduces a new algorithm called MiDAS that extends traditional sequence discovery with a wide range of web-specific features. Domain knowledge is described as flexible Navigation templates that can specify Navigational behavior, as network structures for the capture of web site topologies, in addition to concept hierarchies and syntactic constraints. Unlike existing approaches, field dependency has been implemented, which allows the detection of sequences across monitored attributes, such as URLs and http referrers. Three different types of contained-in relationships are supported, which express different types of browsing behavior. The carried out experimental evaluation have shown promising results in terms of functionality as well as scalability.
Zhao Min-ya - One of the best experts on this subject based on the ideXlab platform.
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Mining user Navigation Pattern using incremental ant colony clustering
Computer Engineering, 2005Co-Authors: Zhao Min-yaAbstract:A novel algorithm for mining user Navigation Pattern with incremental clustering was presented. Firstly, a new method for expressing user interest was introduced to construct user profile object. Based on the basic concept of ant colony clustering, artificial ants were used to pick up or drop down object to implement clustering by analyzing the similarity with other local regional objects and. Then a mechanism of decomposing clusters was used to form new clusters when users' interests changed. Experimental results show that the method can adaptively and efficiently achieve incremental clustering.
P G Om Prakash - One of the best experts on this subject based on the ideXlab platform.
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Predicting the user Navigation Pattern from web logs using weighted support approach
Indonesian Journal of Electrical Engineering and Computer Science, 2021Co-Authors: P G Om Prakash, A. Jaya, Ananthakumaran S, G GaneshAbstract:A weblog contains the history of previous user Navigation Pattern. If the customer accesses any portal of organization website, the log is generated in web server, based on sequence of user transaction. The weblog stored in the web server as unstructured format, it contains both positive and negative responses i.e. successful and unsuccessful responses, identifying the positive and negative response is not useful for identifying user behavior of individual user. Initially the successful response is taken, from that conversion of unstructured log format to structured log format through data preprocessing technique. The process of data preprocessor contains three step process data cleaning, user identification and session identification. The Pattern is discovered by preprocessing technique from that user Navigation Pattern is generated. From that Navigation Pattern classifier technique is applied, the conversion of sequence Pattern to sub sequence Pattern by clustering technique. This research is to identify the user Navigation Pattern from weblog. The Improved Spanning classification algorithm classifies the frequent, infrequent and semi frequent Pattern. To identify the optimal webpage using classificatopn algorithm from thet user behavior is identified.
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Analyzing the User Navigation Pattern from Web Logs Using Maximum Frequent Pattern Approach
2021 6th International Conference on Inventive Computation Technologies (ICICT), 2021Co-Authors: P G Om Prakash, Ananthakumaran S, Sathishkumar M, Ganeshan RAbstract:Web Usage Mining is the application, it automatically discovers the user access Pattern from web servers i.e. logs. The role of the organization is to collect the data from a web server on daily basis. This research work helps to predict the user Navigation Pattern using the classification and clustering technique. In this first stage, this technique identifies the interested potential users from a weblog. In the second stage, the classification technique is applied to classify users having maximum similar interest. In the third stage, the clustering techniques utilize a classifier to identify and predict the user request based on classification. This experimental result will improve the Navigation Pattern based on user behavior. This result will be used for predicting the user request on web sites.
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Analyzing and Predicting User Navigation Pattern from Weblogs using Modified Classification Algorithm
Indonesian Journal of Electrical Engineering and Computer Science, 2018Co-Authors: P G Om Prakash, A. JayaAbstract:A Weblogs contains the history of User Navigation Pattern while user accessing the websites. The user Navigation Pattern can be analyzed based on the previous user Navigation that is stored in weblog. The weblog comprises of various entries like IP address, status code and number of bytes transferred, categories and time stamp. The user interest can be classified based on categories and attributes and it is helpful in identifying user behavior. The aim of the research is to identifying the interested user behavior and not interested user behavior based on classification. The process of identifying user interest, it consists of Modified Span Algorithm and Personalization Algorithm based on the classification algorithm user prediction can be analyzed. The research work explores to analyze user prediction behavior based on user personalization that is captured from weblogs.
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Analyzing the User Navigation Pattern from Weblogs
2014Co-Authors: P G Om Prakash, A. JayaAbstract:In a real world lot of users attracted towards online purchasing, so lots of transactions are going on in the websites. A weblog contains series of entries updated frequently by the user while accessing the website. A Weblog comprises of various entries like IP address, Status Code, number of bytes transferred and timestamp etc. Based on user interest related and unrelated data can be classified. The related data can be considered as success response, while the unrelated data can be considered as failure response. This research work aims to analyze the Pattern of user Navigation while browsing, for that web usage mining must be analyzed. The process of Web Usage Mining consisting steps: Data Collection, Pre-Processing, Pattern Discovery and Pattern Analysis to get user Navigation Pattern that will help us to predict the user behavior and it reduces the mining time.
Sarabjot Singh Anand - One of the best experts on this subject based on the ideXlab platform.
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WEBKDD - User-Driven Navigation Pattern Discovery from Internet Data
Web Usage Analysis and User Profiling, 2000Co-Authors: Matthias Baumgarten, Alex G Buchner, Sarabjot Singh Anand, Maurice Mulvenna, John G HughesAbstract:Managers of electronic commerce sites need to learn as much as possible about their customers and those browsing their virtual premises, in order to maximise the return on marketing expenditure. The discovery of marketing related Navigation Patterns requires the development of data mining algorithms capable of the discovery of sequential access Patterns from web logs. This paper introduces a new algorithm called MiDAS that extends traditional sequence discovery with a wide range of web-specific features. Domain knowledge is described as flexible Navigation templates that can specify generic Navigational behaviour of interest, network structures for the capture of web site topologies, concept hierarchies and syntactic constraints. Unlike existing approaches MiDAS supports sequence discovery from multidimensional data, which allows the detection of sequences across monitored attributes, such as URLs and http referrers. Three methods for pruning the sequences, resulting in three different types of Navigational behaviour are presented. The experimental evaluation has shown promising results in terms of functionality as well as scalability.
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user driven Navigation Pattern discovery from internet data
Lecture Notes in Computer Science, 2000Co-Authors: Matthias Baumgarten, Alex G Buchner, Sarabjot Singh Anand, Maurice Mulvenna, John G HughesAbstract:Managers of electronic commerce sites need to learn as much as possible about their customers and those browsing their virtual premises, in order to maximise the return on marketing expenditure. The discovery of marketing related Navigation Patterns requires the development of data mining algorithms capable of the discovery of sequential access Patterns from web logs. This paper introduces a new algorithm called MiDAS that extends traditional sequence discovery with a wide range of web-specific features. Domain knowledge is described as flexible Navigation templates that can specify generic Navigational behaviour of interest, network structures for the capture of web site topologies, concept hierarchies and syntactic constraints. Unlike existing approaches MiDAS supports sequence discovery from multidimensional data, which allows the detection of sequences across monitored attributes, such as URLs and http referrers. Three methods for pruning the sequences, resulting in three different types of Navigational behaviour are presented. The experimental evaluation has shown promising results in terms of functionality as well as scalability.
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Navigation Pattern discovery from internet data
ACM Workshop on Web Usage Analysis and User Profiling (WebKDD), 1999Co-Authors: Alex G Buchner, Matthias Baumgarten, Sarabjot Singh Anand, Maurice Mulvenna, John G HughesAbstract:Electronic commerce sites need to learn as much as possible about their customers and those browsing their virtual premises, in order to maximize their marketing effort. The discovery of marketing related Navigation Patterns requires the development of data mining algorithms capable of discovering sequential access Patterns from web logs. This paper introduces a new algorithm called MiDAS that extends traditional sequence discovery with a wide range of web-specific features. Domain knowledge is described as flexible Navigation templates that can specify Navigational behavior, as network structures for the capture of web site topologies, in addition to concept hierarchies and syntactic constraints. Unlike existing approaches, field dependency has been implemented, which allows the detection of sequences across monitored attributes, such as URLs and http referrers. Three different types of contained-in relationships are supported, which express different types of browsing behavior. The carried out experimental evaluation have shown promising results in terms of functionality as well as scalability.
